Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Ahmed, SK Razib
Master of Applied Science - MASc
University of British Columbia
This thesis investigates the implementation of flexible functional splits in Cell-Free Massive MIMO (CF-mMIMO) systems under the Open Radio Access Network (O-RAN) framework. CF-mMIMO is a promising architecture for next-generation wireless networks, offering enhanced spectral efficiency (SE), energy efficiency (EE), and consistent service quality. With rising demands from smart devices, the Internet of Things (IoT), and immersive applications like augmented and virtual reality (AR/VR), scalable and efficient wireless solutions are essential. However, integrating CF-mMIMO into O-RAN introduces challenges, including high fronthaul bandwidth due to centralized coordination, increased computational burden at distributed units, and complexity in selecting functional splits that balance latency, power consumption, and processing load. The O-RAN architecture disaggregates conventional baseband processing into standardized functional splits (Split 1 through Split 8), allowing flexible allocation of tasks between centralized and distributed units. This thesis focuses on Split 7.2, which places partial baseband processing at the Radio Unit (O-RU), and Split 8, which centralizes all processing at the Central Unit (O-DU/O-CU). A simulation framework is developed to assess SE, EE, and Jain’s Fairness Index (JFI) across various access point (AP) and antenna configurations using a static user model and coherence block assumptions. Fronthaul limitations and power models are incorporated, and optimization is performed using the Concave-Convex Procedure (CCP). Simulation results show that Split 8 provides superior SE, EE, and fairness due to centralized coordination but requires higher fronthaul capacity and computing resources. Split 7.2, in contrast, reduces fronthaul load and latency, making it suitable for bandwidth-limited or delay-sensitive deploy ments. A fully decentralized architecture is also evaluated and shows significantly poorer performance across all metrics. These findings offer insight into the trade-offs of functional split selection and emphasize how architectural decisions impact network performance. The results support scalable, energy-efficient, and QoS-aware CF-mMIMO networks aligned with the O-RAN paradigm.
Cell-Free Massive Multiple-Input Multiple-Output under theOpen Radio Access Network Flexible Functional Splits towardsEfficient Cellular NetworkbySK RAZIB AHMEDA THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THEREQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinThe College of Graduate Studies(Electrical Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(OKANAGAN)August 2025© SK Razib Ahmed, 2025The following individuals certify that they have read, and recommend to theCollege of Graduate Studies for acceptance, a thesis entitled:Cell-Free Massive Multiple-Input Multiple-Output under theOpen Radio Access Network Flexible Functional Splits towardsEfficient Cellular NetworkSubmitted by SK RAZIB AHMED in partial fulfilment of the requirementsof the degree of Master of Applied ScienceProf. Dr Md. Jahangir Hossain, School of EngineeringSupervisorProf. Dr Stephen O’Leary, School of EngineeringSupervisory Committee MemberProf. Dr Chen Feng, School of EngineeringSupervisory Committee MemberProf. Dr Shawn Xianfu Wang, Department of MathematicsUniversity ExamineriiAbstractThis thesis investigates the implementation of flexible functional splits inCell-Free Massive MIMO (CF-mMIMO) systems under the Open Radio Ac-cess Network (O-RAN) framework. CF-mMIMO is a promising architecturefor next-generation wireless networks, offering enhanced spectral efficiency(SE), energy efficiency (EE), and consistent service quality. With rising de-mands from smart devices, the Internet of Things (IoT), and immersive ap-plications like augmented and virtual reality (AR/VR), scalable and efficientwireless solutions are essential. However, integrating CF-mMIMO into O-RAN introduces challenges, including high fronthaul bandwidth due to cen-tralized coordination, increased computational burden at distributed units,and complexity in selecting functional splits that balance latency, power con-sumption, and processing load.The O-RAN architecture disaggregates conventional baseband processinginto standardized functional splits (Split 1 through Split 8), allowing flexibleallocation of tasks between centralized and distributed units. This thesisfocuses on Split 7.2, which places partial baseband processing at the RadioUnit (O-RU), and Split 8, which centralizes all processing at the CentralUnit (O-DU/O-CU). A simulation framework is developed to assess SE, EE,and Jain’s Fairness Index (JFI) across various access point (AP) and antennaconfigurations using a static user model and coherence block assumptions.Fronthaul limitations and power models are incorporated, and optimizationis performed using the Concave-Convex Procedure (CCP).Simulation results show that Split 8 provides superior SE, EE, and fair-ness due to centralized coordination but requires higher fronthaul capacityand computing resources. Split 7.2, in contrast, reduces fronthaul load andlatency, making it suitable for bandwidth-limited or delay-sensitive deploy-ments. A fully decentralized architecture is also evaluated and shows sig-nificantly poorer performance across all metrics.These findings offer insightinto the trade-offs of functional split selection and emphasize how architec-tural decisions impact network performance. The results support scalable,energy-efficient, and QoS-aware CF-mMIMO networks aligned with the O-RAN paradigm. iiiLay SummaryThis research explores how O-RAN architectures can be combined withCF-mMIMO to improve the speed, reliability, and EE of future mobile net-works. With growing demands from smart devices, the IoT, and immersiveapplications like AR/VR, flexible wireless network solutions are essential.The study focuses on two O-RAN functional splits: Split 7.2 and Split 8.Split 7.2 processes signals closer to the antennas, reducing delay and dataload. Split 8 centralizes all processing, offering better performance but re-quiring more bandwidth and computing power. A fully decentralized setup,where antennas work independently, was also tested.Simulations show that while Split 8 achieves the best performance, itcomes with higher network demands. Split 7.2 offers a balanced alternative,and the decentralized setup performs the worst. These insights help guidehow future mobile networks can be built efficiently and cost-effectively.ivPrefaceThis thesis is based on prior fundamental work on Cell-Free MassiveMIMO and O-RAN functional split architectures. Building upon these estab-lished models and simulation frameworks, I developed an enhanced perfor-mance evaluation methodology to investigate the trade-offs between spectralefficiency, energy efficiency, and fairness under different functional split con-figurations. My contributions include the implementation of a CCP-basedoptimization algorithm, the simulation of both centralized and decentralizeddeployments, and the performance benchmarking of FS 7.2 and FS 8 archi-tectures across varying antenna and access point configurations.With the approval of my supervisor, I used Generative Artificial Intel-ligence (GenAI) tools — specifically ChatGPT-4 — in a limited capacityto refine grammar, improve technical clarity, and support MATLAB codedebugging. All enhancements, experimental configurations, result interpre-tations, and conclusions are solely my own.vTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . viList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xList of Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiGlossary of Mathematical Symbols . . . . . . . . . . . . . . . . . xvAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii1 Introduction 11.1 Background and Motivation . . . . . . . . . . . . . . . . . . . 11.2 Key Enabling Technologies . . . . . . . . . . . . . . . . . . . . 41.3 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . 51.4 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.5 Methodology Overview . . . . . . . . . . . . . . . . . . . . . . 51.6 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . 62 Literature Review and Research Contributions 72.1 Introduction to the Literature Review . . . . . . . . . . . . . . 72.2 Open Radio Access Network (O-RAN) . . . . . . . . . . . . . 82.2.1 Open RAN (O-RAN) . . . . . . . . . . . . . . . . . . . 82.2.2 Evolution of the RAN . . . . . . . . . . . . . . . . . . 92.2.3 O-RAN Key Architecture and Interfaces . . . . . . . . 11vi2.2.4 RAN Intelligent Controllers (Non-RT RIC and Near-RT RIC) . . . . . . . . . . . . . . . . . . . . . . . . . . 162.2.5 O-RAN Interfaces . . . . . . . . . . . . . . . . . . . . . 192.2.6 O-RAN Functional Split . . . . . . . . . . . . . . . . . 202.3 Cell-Free Massive MIMO . . . . . . . . . . . . . . . . . . . . . 302.3.1 Introduction and Evolution of Wireless Systems . . . . 302.3.2 Architectural Framework . . . . . . . . . . . . . . . . . 342.3.3 Channel Modeling and Fading Environments . . . . . . 352.3.4 Uplink and Downlink Transmission . . . . . . . . . . . 382.3.5 Channel Estimation and Pilot Contamination . . . . . 422.3.6 Spectral Efficiency and Power Control . . . . . . . . . 442.3.7 Scalability and Decentralization . . . . . . . . . . . . . 462.3.8 Energy Efficiency Models . . . . . . . . . . . . . . . . . 482.3.9 Integration with O-RAN . . . . . . . . . . . . . . . . . 502.3.10 Summary of CF-mMIMO Literature . . . . . . . . . . 552.4 Research Gap . . . . . . . . . . . . . . . . . . . . . . . . . . . 562.5 Research Contribution . . . . . . . . . . . . . . . . . . . . . . 57Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573 Effect of Functional Split on the Integrated Cell-Free mMIMOwith O-RAN 593.1 Architectural Configurations of CF-mMIMO Under O-RANFramework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 593.2 Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . . 643.2.1 Uplink . . . . . . . . . . . . . . . . . . . . . . . . . . . 643.2.2 Downlink . . . . . . . . . . . . . . . . . . . . . . . . . 643.3 Functional Splits and Fronthaul Modeling . . . . . . . . . . . 653.4 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . 663.5 Proposed Algorithm . . . . . . . . . . . . . . . . . . . . . . . 733.5.1 CCP-Based Iterative Optimization . . . . . . . . . . . 743.5.2 Refinement Stage via Sparse Power Vector Recovery . . 764 Simulation Setup, Results and Discussion 794.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . 794.1.1 Fully Centralized Architecture (FS 8) . . . . . . . . . . 794.1.2 Partially Centralized Architecture (FS 7.2) . . . . . . . 804.1.3 Fully Decentralized Architecture (Non-O-RAN) . . . . 804.2 Spectral Efficiency (SE) Trends . . . . . . . . . . . . . . . . . 80vii4.3 Energy Efficiency (EE) Trends . . . . . . . . . . . . . . . . . . 854.4 Fairness Analysis (Jain’s Fairness Index - JFI) . . . . . . . . . 894.5 SE, EE, and JFI Comparison for Centralized vs. DecentralizedSetup for Specific AP Counts . . . . . . . . . . . . . . . . . . 944.5.1 Spectral Efficiency (SE) . . . . . . . . . . . . . . . . . 954.5.2 Energy Efficiency (EE) . . . . . . . . . . . . . . . . . . 964.5.3 Jain’s Fairness Index (JFI) . . . . . . . . . . . . . . . . 974.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 995 Discussion and Future Work 1015.1 Key Observations and Deployment Considerations . . . . . . . 1015.2 Flexibility and Real-World Implementation . . . . . . . . . . . 1025.3 Limitations of the Study . . . . . . . . . . . . . . . . . . . . . 1035.4 Future Work and Research Directions . . . . . . . . . . . . . . 1035.5 Final Thoughts . . . . . . . . . . . . . . . . . . . . . . . . . . 105Bibliography 106Appendices 113Appendix A: Fronthaul Bandwidth Comparison Under Split 7.2 andSplit 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114Appendix B: Glossary of Notation . . . . . . . . . . . . . . . . . . . 116viiiList of Tables1.1 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . 62.1 O-RAN Interfaces and Their Functions . . . . . . . . . . . . . 202.2 Summary of O-RAN Functional Split Options For Splits 6–8,most processing responsibilities are handled by the DU and RU. 303.1 Comparison of CF-mMIMO Architectural Configurations . . . 634.1 SE, EE, and JFI Comparison – Centralized vs. DecentralizedSetup for All Weighting Scenarios . . . . . . . . . . . . . . . . 944.2 SE, EE, and JFI Performance Comparison – Centralized vs.Decentralized FS 8 for Various AP Counts andWeighting Con-figurations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 995.1 ETSI Traffic Load Weighting Factors for 24-Hour Power Av-eraging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104A.1 Parameters for fronthaul bandwidth calculation . . . . . . . . 114ixList of Figures1.1 Evolution from Distributed RAN to O-RAN and CF-mMIMO,enabling 6G Key Performance Indicators (KPIs). . . . . . . . 21.2 OPEX distribution in mobile networks with emphasis on en-ergy costs. Source: Arthur D. Little (2021) [1]. . . . . . . . . . 42.1 Overview of the evolution of the RAN in mobile networks,from D-RAN to O-RAN. . . . . . . . . . . . . . . . . . . . . . 112.2 O-RAN architecture with RU, DU, CU-CP, and CU-UP mod-ules [2]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.3 O-RAN Functional Units and Interfaces [2]. . . . . . . . . . . 152.4 RAN Intelligent Controller (RIC) overview [2]. . . . . . . . . 182.5 O-RAN Functional Split [3] . . . . . . . . . . . . . . . . . . . 212.6 Traditional cellular network coverage showing lower data rateat the cell edges [4]. . . . . . . . . . . . . . . . . . . . . . . . 312.7 User-centric clustering in CF-mMIMO showing APs coordi-nated by CPUs and serving overlapping UE groups [4]. . . . . 322.8 Comparison of data rate distribution: unlike the cellular case(Fig. 2.6), CF-mMIMO provides nearly uniform throughputacross the entire service area. . . . . . . . . . . . . . . . . . . 332.9 Structure of a coherence block illustrating pilot and data trans-mission phases [5]. . . . . . . . . . . . . . . . . . . . . . . . . 372.10 Uplink transmission in CF-mMIMO showing signals from UEsto APs and centralized combining techniques. . . . . . . . . . 392.11 Downlink transmission in CF-mMIMO showing APs transmit-ting precoded symbols to UEs using linear precoding technique. 412.12 Breakdown of total power consumption components in CF-mMIMO systems. . . . . . . . . . . . . . . . . . . . . . . . . . 502.13 Integration architecture of CF-mMIMO within the O-RANframework [6]. . . . . . . . . . . . . . . . . . . . . . . . . . . . 52x2.14 Functional splits in O-RAN architecture (7.2 and 8) mappedto uplink and downlink processing chains. . . . . . . . . . . . 533.1 Cell-Free mMIMO with O-RAN FS 8 Deployment . . . . . . . 603.2 Cell-Free mMIMO with O-RAN FS 7.2 Deployment . . . . . . 613.3 Decentralized Deployment without O-RAN . . . . . . . . . . . 624.1 SE vs. AP count (L) for antennas N = 4. . . . . . . . . . . . 814.2 SE vs. AP count (L) for antennas N = 8. . . . . . . . . . . . 824.3 SE comparison: Centralized (FS 8) vs. Decentralized for an-tenna N = 8. . . . . . . . . . . . . . . . . . . . . . . . . . . . 844.4 EE vs. AP (L) for antenna N = 4. . . . . . . . . . . . . . . . 864.5 EE vs. AP (L) for antennas N = 8. . . . . . . . . . . . . . . . 874.6 EE comparison: Centralized (FS 8) vs. Decentralized for an-tennas N = 8. . . . . . . . . . . . . . . . . . . . . . . . . . . . 884.7 JFI vs. number of APs (L), for N = 4, comparing FS 7.2 andFS 8. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 904.8 JFI vs. number of APs (L), for N = 8, comparing FS 7.2 andFS 8. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 914.9 The JFI versus the number of APs for Centralized (FS 8) vs.Decentralized for antennas N = 8 . . . . . . . . . . . . . . . . 934.10 SE – Comparison for Centralized vs. Decentralized (N = 8)at L = 20, 30, 40 for Maximum Priority . . . . . . . . . . . . 964.11 EE – Centralized FS 8 vs. Decentralized, Antenna (N = 8),AP Count L = 20, 30, 40 for Maximum Priority. . . . . . . . . 974.12 JFI – Comparison for Centralized vs. Decentralized FS 8 (N =8) at L = 20, 30, 40 for Maximum Priority . . . . . . . . . . . 98A.1 Maximum Supported APs per DU under 20 Gbps FronthaulBudget for Split 7.2 and Split 8 . . . . . . . . . . . . . . . . . 115xiList of Acronyms3GPP 3rd Generation Partnership Project3GPP TR 3GPP Technical ReportAI Artificial IntelligenceAP Access PointAR Augmented RealityBBU Baseband UnitCF-mMIMO Cell-Free Massive MIMOCSI Channel State InformationCU Central UnitDU Distributed UnitEE Energy EfficiencyJFI Jain’s Fairness IndexMIMO Multiple Input Multiple OutputO-CU Open Centralized UnitO-DU Open Distributed UnitO-RAN Open Radio Access NetworkO-RU Open Radio UnitQoS Quality of ServicexiiRIC RAN Intelligent ControllerSE Spectral EfficiencySINR Signal-to-Interference-plus-Noise RatioUE User EquipmentCU-CP Centralized Unit – Control PlaneCU-UP Centralized Unit – User PlanePDCP Packet Data Convergence ProtocolSDAP Service Data Adaptation ProtocolRRC Radio Resource ControlNon-RT RIC Non-Real-Time RAN Intelligent ControllerNear-RT RIC Near-Real-Time RAN Intelligent ControllereCPRI Enhanced Common Public Radio InterfaceOLT Optical Line TerminalONU Optical Network UnitOPEX Operational ExpenditureIoT Internet of ThingsAR/VR Augmented and Virtual RealityFS Functional SplitFR1/FR2 Frequency Range1 and Frequency Range2CPU Central Processing UnitMAC Medium Access ControlxApp Near-Real-Time RIC ApplicationrApp Non-Real-Time RIC ApplicationxiiiCAPEX Capital ExpenditureTCO Total Cost of OwnershipRLC Radio Link ControlHARQ Hybrid Automatic Repeat RequestFFT Fast Fourier TransformIFFT Inverse Fast Fourier TransformxivMathematical Symbols andVariablesplk Downlink power allocated from AP l to UE k.pl Vector of downlink power values from AP l to all UEs.∥pl∥22 Total downlink transmission power from AP l.zl Binary or relaxed indicator variable showing if AP l is active(1) or inactive (0).XLPk Baseband processing load corresponding to user k.Pproc Static power consumption of baseband processor.∆proc Incremental power consumption per unit processing load.GOPSmax Maximum processing capacity in Giga Operations PerSecond.σcool Cooling system efficiency factor.Pfixed(zl) Static power based on AP activation.Pload(plk, zl) Load-dependent power based on power allocation and APstatus.Ptotal Total power consumption (fixed + load-dependentcomponents).τc Length of the coherence block in symbols.τp Length of pilot sequence in symbols.preLogFactor Fraction of symbols for data transmission: τc−τpτc.α Interference scaling factor to model coordination loss indecentralized setups (typically 1.3).β Channel gain degradation factor for decentralized setups(typically 0.8).xvCN (µ,R) Circularly symmetric complex Gaussian distribution withmean µ and covariance matrix R. For µ = 0 and R = γlkI,this models Rayleigh fading with variance γlk per antenna.hlk Channel vector between AP l and user k under small-scalefading; modeled as CN (0, γlkI) for N antennas.γlk Large-scale fading coefficient representing path loss andshadowing between AP l and user k.hˆlk Estimated channel vector from AP l to user k.Hˆ Aggregated estimated channel matrix used for linearprecoding in the downlink.Yp,l Received pilot signal matrix at AP l, of dimension N × τp.ϕk Pilot sequence for user k, of length τp.µ Regularization parameter used in MMSE precoding tobalance interference suppression and noise amplification.xviAcknowledgementsI would like to sincerely thank Prof. Md. Jahangir Hossain for his excel-lent guidance and constant support during this research. His valuable advice,regular meetings, and strong mentorship were crucial in helping me completethis work successfully. I am truly grateful for the freedom that he gave meto explore my ideas while keeping my work focused and well-structured.I also want to give special thanks to Hasabel Naby, Bayan Al Nahhasand Sarah Ahmed Bahanshal for their time and effort in checking my results,improving technical details, and helping me organize the report better. Theirfeedback made my work much clearer and stronger. I am also grateful to myfriends and colleagues for their support and helpful discussions, which madethis journey more enjoyable.Finally, my greatest thanks go to my parents and family. They have sup-ported me through every step of my journey, cheering me on even when thingswere tough. Their love and faith in me have been my greatest inspiration.xviiChapter 1Introduction1.1 Background and MotivationThe mobile communications landscape is rapidly evolving, driven by expo-nential growth in user demand, connected devices, and high-throughputapplications. According to mobile traffic forecasts by ITU-R and Erics-son, global mobile traffic is projected to exceed 5000 exabytes (1 EB =1018 bytes ≈ 106 TB = 109 GB) per month by 2030, with new use casesdemanding ultra-reliable low-latency communication, massive machine-typeconnectivity, and high Spectral Efficiency (SE).To meet these demands, network architectures must evolve beyondthe limitations of traditional monolithic Base Station (BS). Emergingparadigms such as Open Radio Access Network (O-RAN) and Cell-Free Massive MIMO (CF-mMIMO) offer promising solutions. O-RANintroduces disaggregated software-defined RAN architectures with standard-ized open interfaces, enabling flexible and vendor-neutral deployments. CF-mMIMO, on the other hand, removes the concept of cells entirely, distribut-ing access points (AP) throughout the coverage area and coordinating themthrough centralized processing to collaboratively serve users.The integration of CF-mMIMO with the O-RAN architecture holds signif-icant promise. Although CF-mMIMO ensures fairness and improved spectralefficiency (SE) through user-centric AP coordination, O-RAN supports thiswith modular functional split (FS) options, particularly FS 7.2 and FS 8,which trade off fronthaul complexity and coordination capabilities. Together,1these technologies enable adaptive, intelligent, and cloud-native deploymentsthat can scale toward 6G requirements—such as 1 Tb/Joule Energy Effi-ciency (EE), sub-millisecond latency, and ultra-high connection density—asoutlined in the IMT-2030 framework by ITU-R [7] and the ongoing 3GPPstudy item on 6G scenarios and requirements (RP-243327) [8].Figure 1.1 illustrates the architectural evolution from traditional dis-tributed RAN to modular O-RAN and CF-mMIMO deployments. Thisprogression is aligned with the 3GPP standardization roadmap: 4G LTEwas first standardized in Release 8 (2008) with subsequent enhancementsup to Release 14 (2017); 5G New Radio (NR) was introduced in Release 15(2018) and further advanced in Releases 16 and 17 (2019–2021). The on-going 5G-Advanced standardization spans Releases 18–20 (2023–2026), withcommercial operation expected through 2030. In parallel, 6G developmentis forecasted to start with Release 21 (2027) and Release 22 (2029), target-ing commercial deployment around 2030. These standardization milestonessupport key 6G performance indicators such as extreme energy and spec-tral efficiency, sub-millisecond latency, and ultra-high user density, enabledthrough O-RAN and CF-mMIMO architectures.Figure 1.1: Evolution from Distributed RAN to O-RAN and CF-mMIMO,enabling 6G Key Performance Indicators (KPIs).2Although performance and flexibility are key enablers, energy con-sumption is becoming an equally pressing concern. As telecom infrastruc-ture densifies and centralization increases, operational costs, particularlythose related to energy, are being increasingly scrutinized. According toArthur D. Little’s 2021 point of view, electricity accounts for approximately14% of the total Operational Expenditure (OPEX) of a mobile opera-tor [1], a figure that is expected to increase further with the introduction ofhigh capacity 5G and 6G networks.Centralized architectures like FS 8 offer an opportunity to mitigate thesechallenges. By consolidating signal processing at fewer central sites, FS 8 canimprove hardware utilization, reduce redundant energy use across distributedsites, and align with Environmental, Social, and Governance (ESG)through lower carbon emissions. In contrast, Split 7.2 balances fronthaulefficiency with moderate centralization but may miss out on certain coordi-nation and energy gains.To underscore the importance of energy-aware design, Figure 1.2 presentsthe OPEX breakdown for mobile networks, showing that electricity aloneconstitutes a substantial and growing share. This reinforces the thesis moti-vation for optimizing EE under FS architectures.3Figure 1.2: OPEX distribution in mobile networks with emphasis on energycosts. Source: Arthur D. Little (2021) [1].Given these trends, this thesis is motivated by the need to jointly optimizeSE, EE and user fairness in CF-mMIMO deployments under realistic O-RANFS constraints. The work aims at a future-ready architecture that balancesperformance with sustainability and deployment feasibility.1.2 Key Enabling TechnologiesThis thesis focuses on two enabling technologies: O-RAN architecture andCF-mMIMO. O-RAN provides openness, virtualization, and centralized con-trol, while CF-mMIMO leverages distributed APs coordinated to jointly serveusers. Split 7.2, as described in 3rd Generation Partnership ProjectTechnical Report (3GPP TR) 38.801 and the O-RAN Alliance whitepapers, performs partial PHY-layer processing at the Distributed Unit(DU), while Split 8 centralizes the entire PHY stack at the CentralizedUnit (CU), requiring greater fronthaul bandwidth but enabling coordinatedprocessing [9, 10].41.3 Problem StatementAlthough CF-mMIMO and O-RAN have been studied independently, limitedresearch has focused on their integration. The impact of O-RAN FS 7.2 vs.FS 8 on fronthaul efficiency, processing load, energy usage, and fairness inCF-mMIMO scenarios is not well-understood. This thesis addresses thisresearch gap through detailed modeling and simulation.1.4 ObjectivesThe primary objectives of this thesis are:• Develop a simulation framework to evaluate CF-mMIMO with O-RANunder FS 7.2 and FS 8.• Compare SE, EE, and fairness for centralized and decentralized deploy-ments across antenna and AP configurations.• Quantify the impact of fronthaul capacity and local processing on sys-tem performance.• Use empirical degradation parameters (e.g., α, β) to model decentral-ized performance penalties.1.5 Methodology OverviewThe simulation is performed in MATLAB for a CF-mMIMO system comprisingK single-antenna users, L distributed access points (APs), and N antennasper AP. The values for these parameters, along with other system-level set-tings used in this thesis, are summarized in Table 1.1. A total bandwidth of20 MHz is assumed at a carrier frequency of 2.6 GHz. Monte Carlo simu-lations are performed on 30 independent deployments, with 500 small-scalechannel realizations per deployment.5Parameter ValueNumber of users (K) 16Number of access points (L) 20 to 50Number of antennas per AP (N) 4 and 8Carrier frequency 2.6 GHzSystem bandwidth 20 MHzNumber of deployments 30Channel realizations per deployment 500Table 1.1: Simulation ParametersThe performance metrics evaluated include SE, EE, and JFI. To real-istically model decentralized CF-mMIMO setups, degradation parametersα = 1.3 (increased interference due to limited coordination) and β = 0.8(reduced effective channel gain) are applied, consistent with values reportedin prior literature [11, 12].1.6 Thesis Organization• Chapter 1: Introduction - Background, motivation and methodol-ogy.• Chapter 2: Literature Review and Research Contributions —Reviews O-RAN, CF-mMIMO, and identifies gaps.• Chapter 3: System Model—Details the signal model, architecturalassumptions, and optimization framework.• Chapter 4: Simulation Results and Discussion — Presents SE,EE, and fairness trends across scenarios.• Chapter 5: Conclusion and Future Work— Summarizes findingsand proposes future research.6Chapter 2Literature Review andResearch Contributions2.1 Introduction to the Literature ReviewThis chapter presents a comprehensive review of two fundamental technolo-gies that are driving the evolution of next-generation wireless networks: O-RAN and CF-mMIMO. As the limitations of traditional cellular architec-tures—such as rigid cell boundaries, vendor lock-in, and limited SE—becomeincreasingly apparent, the research community has shifted focus toward moreflexible, scalable, and energy-efficient alternatives.O-RAN aims to transform the radio access ecosystem by introducingopenness, disaggregation, and software-defined intelligence. This paradigmpromotes vendor interoperability, fosters innovation, and enables a more pro-grammable and adaptable infrastructure. In parallel, CF-mMIMO eliminatesthe notion of fixed cells, allowing a large number of distributed APs to jointlyserve users in a coordinated fashion. This architecture enhances SE, improvesuser fairness, and leverages macro-diversity benefits.This review of the literature synthesizes the latest academic research,industry developments, and standardization efforts related to O-RAN andCF-mMIMO. It establishes the theoretical and practical foundations neces-sary to support the objectives, formulation of the problem, and methodologyof this thesis.72.2 Open Radio Access Network (O-RAN)2.2.1 Open RAN (O-RAN)Cellular networks are becoming ever more complex with the advent of inno-vative technologies such as network slicing, massive multiple-input multiple-output (MIMO), multi-band and multi-technology support, millimeter wave,and machine learning-based developments. Additionally, the expected surgein both data volume and diversity further contributes to the growing com-plexity of these networks. Network operators are under pressure to keep upwith the market trends by encompassing newer technologies, continuouslyupgrading and maintaining their networks while keeping the cost per bit aslow as possible. To reduce expenses and simultaneously roll out more ad-vanced technology solutions, mobile operators have begun to investigate newrevenue-generating services.Recently, the core network has seen significant changes with the adventof software-defined networking (SDN) and network function virtualization(NFV), enabling operators to build a more agile and cost-effective core. How-ever, the RAN—which involves the majority of mobile network costs—hasremained relatively untouched in terms of cost optimization, despite notableadvancements. Indeed, in typical mobile network cost breakdowns, the RANconstitutes the largest share of both capital and operational expenditures;for example, it accounts for approximately 65 % of OPEX and dominatesCAPEX as well [13]. In fact, industry analyses suggest that the RAN drivesaround 60 % or more of total cost of ownership (TCO) [14], and can ac-count for up to 70 % of the network’s TCO in some assessments [15]. Thisunderlines the critical need to optimize RAN costs to reduce overall TCO.Consequently, there is an urgent need for a paradigm shift in the RANdomain to foster a more balanced, open, and economically sustainable ecosys-tem. Despite advances in network virtualization, most RAN deploymentsstill rely on monolithic, vendor-specific solutions with tightly coupled pro-prietary baseband and radio units. These systems often feature closed andundocumented interfaces, resulting in vendor lock-in where operators are ef-fectively constrained to using the same vendor’s equipment within specificdeployment zones to avoid interoperability issues, such as handover failuresor degraded performance in multi-vendor environments. This dependency8limits flexibility and introduces supply chain risks that can disrupt servicecontinuity.The RAN equipment domain is currently dominated by a few vendorsand is often seen by network operators as a black box, lacking transparency.These systems often lack advanced data-driven optimization and closed-loopcontrol capabilities necessary to manage complex, heterogeneous networkseffectively. Even vendor-specific closed-loop optimization techniques provideminimal operator control, which impacts customer service quality.To address the limitations of traditional RAN systems, O-RAN hasemerged through joint efforts by industry and standardization bodies as amodular and forward-looking RAN paradigm. It is founded on the principlesof disaggregation, where RAN functionalities are decomposed into virtual-ized, software-defined components interconnected via standardized open in-terfaces. These components can be flexibly managed and orchestrated usingintelligent control frameworks. The O-RAN model aims to introduce cloud-native scalability, cost-efficiency, and operational agility into the radio accessdomain [10].Virtualization and disaggregation enable flexible deployments based oncloud-native principles. This improves the RAN’s resilience and reconfig-urability. Open standardized interfaces allow multi-vendor deployments andenable smaller vendors to participate in the RAN ecosystem, fostering amore competitive and vibrant supplier system. Intelligent controllers lever-aging open interfaces can significantly enhance network automation, makingRAN optimization more efficient, self-driven, cost-effective, and accessiblefor network operators [16, 2].2.2.2 Evolution of the RANThe RAN architecture has evolved significantly over time: from the tradi-tional distributed RAN (D-RAN), to centralized/cloud RAN (C-RAN), andmore recently to virtualized RAN (vRAN) and Open RAN (O-RAN). D-RAN features integrated baseband units located close to the radio units,while C-RAN centralizes baseband processing to improve coordination ef-ficiency. With vRAN, these functions are virtualized—typically running ongeneral-purpose cloud infrastructure. O-RAN further disaggregates hardware9and software, and specifies open, interoperable interfaces in line with 3GPPNext Generation RAN splits such as the 7.2x fronthaul split [17, 18, 19].While 3GPP defines the functional units and interface splits between CU,DU, and RU (beginning with Release 15, frozen in June 2018 and fully spec-ified by September 2019) [17, 20], the O-RAN Alliance complements this bypublishing detailed specifications for open fronthaul, E2, and O1 interfaces,with more than 130 technical documents released to date, including 67 newor updated documents since late 2024 [21].Decentralized RAN (D-RAN): In the D-RAN architecture, both theradio unit (RU) and the baseband unit (BBU) are co-located at the cell site,with all signal processing executed locally. The RU is responsible for trans-mitting and receiving radio signals to and from user devices, while the BBUperforms baseband processing tasks such as modulation, demodulation, andresource scheduling. Typically, the BBU is placed in a climate-controlledshelter, whereas the RU is installed near the antenna or at the top of thetower. In most cases, both units are proprietary, limiting vendor interoper-ability. As the number of user equipment (UEs) increases, more base stations(BSs) are required, thereby escalating costs associated with physical space,power consumption, and cooling.Centralized RAN (C-RAN): To reduce infrastructure expenditures,the centralized RAN (C-RAN) concept was introduced. In C-RAN, BBUsfrom multiple sites are centralized at a single location, reducing operationalexpenses such as site rental and maintenance. New RUs can be deployedwithout installing new BBUs at each site, allowing for scalable networkexpansion. However, the architecture retains proprietary elements acrossBBUs, RUs, and the fronthaul interface. Limitations include high fronthaulbandwidth requirements, centralized failure risks, and continued vendor lock-in. As a result, C-RAN is primarily suited to urban environments.Virtualized RAN (vRAN): Virtualized RAN (vRAN) extends C-RANby replacing proprietary BBU hardware with COTS servers and applying net-work function virtualization (NFV) principles. The software stack is decou-pled from hardware and runs on virtual machines or containers. While thisallows pooling and sharing of BBU resources across multiple sites—reducingthe data processing footprint by up to 50%—the fronthaul interface remainsproprietary, and radio node-level resource sharing becomes more complex10due to network virtualization overhead.Open RAN (O-RAN): The transition from vRAN to O-RAN intro-duces standardized interfaces while adopting cloud-native principles fromvRAN. A critical innovation is the Open Fronthaul (Open FH) interface be-tween the RU and BBU. O-RAN also supports the disaggregation of thetraditional BBU into a DU and a CU. Standardized interfaces empower op-erators to mix and match multi-vendor components such as RUs, DUs, andCUs to tailor networks to specific deployment scenarios.Figure 2.1 illustrates this evolution of the RAN architecture—from tradi-tional D-RAN and centralized C-RAN to virtualized and fully disaggregatedO-RAN systems—highlighting key transitions such as centralization, virtu-alization, and openness.Figure 2.1: Overview of the evolution of the RAN in mobile networks, fromD-RAN to O-RAN.Moreover, O-RAN facilitates the replacement of proprietary RU hardwarewith COTS-based software-defined radios (SDRs) and supports the decou-pling of the control and user planes. By integrating AI/ML-driven intelli-gence into the RAN controller, O-RAN enables efficient, autonomous, andscalable network operations.2.2.3 O-RAN Key Architecture and InterfacesThe key architectural principles of O-RAN can be identified as four coreelements: Virtualization, Disaggregation, Open Interfaces, and Intelligence,each contributing to a flexible, cost-effective, and scalable mobile networkdeployment.Virtualization: Virtualization of the RAN refers to the concept of de-coupling the hardware and software used to build the RAN. Virtualization11makes it possible to run the RAN functionality as (open) software runningon generic hardware platforms, rather than using proprietary hardware. Oneof the benefits of virtualization is that it allows companies to use the best-of-breed and cost-effective hardware. Secondly, it will enable companies toaddress changing network demands by elastically scaling resources. Virtu-alization also has the potential to simplify RAN orchestration through lay-ered abstraction, thus contributing to a significant reduction in operationalcosts [2].Disaggregation: Disaggregation allows splitting the BS into several func-tional units. In 5G networks, the BS can be divided into three logical units:• Radio Unit (RU) – handles radio frequency (RF) processing and lowerphysical (Low-PHY) layer functions.• Distributed Unit (DU) – executes higher physical (High-PHY) layerfunctions, Medium Access Control (MAC), and Radio Link Control(RLC).• Centralized Unit (CU) – further divided into:– CU-CP (Centralized Unit – Control Plane): manages the control-plane functions such as the Radio Resource Control (RRC) andcontrol-plane Packet Data Convergence Protocol (PDCP).– CU-UP (Centralized Unit – User Plane): handles user-plane func-tions including user-plane PDCP and the Service Data AdaptationProtocol (SDAP).Such a logical separation facilitates the deployment of diverse functional-ities across various network locations and hardware platforms [10].Figure 2.2 illustrates the O-RAN architecture, including standardized in-terfaces such as O1, A1, E1, E2, and F1. These interfaces play a critical rolein enabling interoperability, disaggregated control, and real-time manage-ment within the RAN. The O1 interface connects the Service Managementand Orchestration (SMO) layer to the RAN components for managementfunctions. The A1 interface links the Non-Real-Time RAN IntelligentController (Non-RT RIC) to the Near-Real-Time RAN IntelligentController (Near-RT RIC), supporting policy exchange and Machine12Figure 2.2: O-RAN architecture with RU, DU, CU-CP, and CU-UP mod-ules [2].Learning (ML) model updates. The E1 interface enables communicationbetween the control plane CU-CP and CU-UP components. The E2 interfacefacilitates near-real-time control by connecting the Near-RT RIC to E2 nodessuch as the CU and DU. Finally, the F1 interface connects the DU to theCU, supporting FS as defined by 3GPP. The Near-RT RIC hostsNear-Real-Time RIC Applications (xApps) that execute near-real-time control al-gorithms such as scheduling and mobility management, while the Non-RTRIC hosts Non-Real-Time RIC Applications (rApps) responsible fornon-real-time tasks like policy optimization and training of ML models.Open Interfaces: Open Interfaces refers to the standardized interfacesbetween different components of a RAN, which allow for interoperability13between equipment from different vendors. This enables the mixing andmatching of RAN equipment from various vendors, thereby combining themost suitable equipment for a given deployment scenario, considering factorssuch as performance, required features, schedule and costs [2].Intelligence: The RAN Intelligent Controller (RIC) is a key elementof the O-RAN architecture that provides a platform for optimizing the RANelements and resources. The RIC is implemented in two distinct forms: theNon-RT RIC, designed for non-real-time control and long-term optimization,and the Near-RT RIC, intended for control and optimization tasks in thenear-real-time domain.Both RICs host third-party applications that leverage machine learning(ML) and artificial intelligence (AI) techniques to orchestrate RAN behav-ior based on specific use cases. These applications, known as xApps andrApps, are tailored algorithms designed for tasks such as Radio ResourceManagement (RRM) and Self-Organizing Network (SON) functionalities.The integration of these intelligent external controllers into the RANinfrastructure significantly enhances the ability to manage and orchestratenetwork services efficiently and cost-effectively [22, 23].Component Overview:O-RU (Open Radio Unit): The O-RU is typically deployed at the cellsite and is responsible for interfacing with end-user devices. It processesradio frequency (RF) signals and handles the lower part of the PhysicalLayer (Low-PHY) [2].O-DU (Open Distributed Unit): The O-DU, usually located closer tothe cell site to meet latency and environmental requirements, manages com-munication with multiple O-RUs. It handles the upper part of the PhysicalLayer (High-PHY), MAC, and RLC [2].O-CU (Open Centralized Unit): The O-CU is a logical node that hoststhe RRC, PDCP, and SDAP protocols and provides Layer 3 functions suchas connection and mobility management. It is further divided into:14• O-CU-UP: Responsible for user plane processing (PDCP and SDAP)• O-CU-CP: Manages control plane functions (RRC and control PDCP)The O-DU connects to O-CU-CP and O-CU-UP via the F1-C and F1-Uinterfaces, respectively. O-CU-CP also connects to the Access and MobilityManagement Function (AMF) in the 5G Core (5GC), while the O-CU-UPinterfaces with the User Plane Function (UPF) [2].O-Cloud. The O-Cloud is a cloud-native platform comprising multiplephysical infrastructure nodes that meet the requirements for hosting O-RANfunctions such as the Near-RT RIC, Non-RT RIC, O-CU-CP, O-CU-UP, andO-DU. The O-Cloud also includes supporting software components such asoperating systems, virtual machines, container runtimes, and orchestrationfunctions.Figure 2.3 provides a visual overview of the key O-RAN compo-nents—including O-RU, O-DU, and O-CU—and their interconnecting in-terfaces as defined by the O-RAN Alliance [2].Figure 2.3: O-RAN Functional Units and Interfaces [2].15The underlying hardware infrastructure of the O-Cloud integrates com-pute, network, and storage resources, potentially augmented by hardwareaccelerators to meet real-time processing requirements. Furthermore, the O-Cloud exposes well-defined APIs to orchestrate the deployment lifecycle ofnetwork functions as well as broader infrastructure resources [2].SMO (Service Management and Orchestration). In a serviceprovider’s network, domains like RAN, Core, Transport, and Slice Manage-ment coexist. Among the various domains, SMO is specifically tasked withmanaging the RAN domain in the O-RAN architecture. It acts as the orches-tration and management framework responsible for lifecycle and performancemanagement.The SMO uses several management interfaces:• O1: For FCAPS (Fault, Configuration, Accounting, Performance, Se-curity) of all RAN elements except the O-RU.• O2: For managing O-Cloud infrastructure resources and virtual work-loads.• O-FH M-Plane: For managing the O-RU directly.• A1: For interfacing with the Near-RT RIC (via the Non-RT RIC),particularly for the RRM.These components work together to enable a fully disaggregated, orches-trated, and intelligent RAN [2, 22].2.2.4 RAN Intelligent Controllers (Non-RT RIC andNear-RT RIC)One of the most crucial components of the O-RAN architecture is the RIC.As the name implies, its purpose is to introduce intelligence into RAN oper-ations. These controllers collect telemetry data from RAN components andexecute control actions based on AI-driven analytics. By leveraging artificialintelligence (AI) and machine learning (ML), RICs enable feedback-drivencontrol and optimization through closed-loop architectures.16RICs are designed to be extensible platforms that can host multiple cus-tom logic applications. These applications allow network operators to man-age different tasks or use cases dynamically, thus making the RIC a highlyflexible and future-proof component of the O-RAN ecosystem.Figure 2.4 illustrates the functional architecture of the RAN IntelligentControllers (RICs), highlighting the separation between the Non-RT RICand Near-RT RIC, as well as their interaction through the standardized A1and E2 interfaces. The Non-RT RIC, situated within the SMO, hosts rApps(e.g., rApp1–3) that support non-real-time functions. The Near-RT RIChosts xApps (e.g., xApp1–3) responsible for near-real-time control. The R1interface provides enrichment data from the SMO to the Non-RT RIC, whilethe O1 interface supports management communication. The E2 Node at thebottom represents the RAN functions controlled via the E2 interface. De-tailed roles and functionalities of xApps, rApps, and the RICs are discussedin the following sections.There are two primary types of RICs in the O-RAN architecture [22]:Near-Real-Time RIC (Near-RT RIC). The Near-RT RIC is deployedas a virtualized function on the O-Cloud platform. It operates in feedbackloops ranging from 10 milliseconds to 1 second and provides control andmanagement over the network in near-real-time. The Near-RT RIC hostsxApps—custom logic modules that collect data, perform real-time computa-tions, and dispatch control actions to RAN functions.These xApps enable intelligent features such as• Traffic steering• Quality of Service (QoS)-based resource allocation• Beam mobility managementIts operations are influenced by policies, ML models, and enrichment dataprovided by the Non-RT RIC [24].Non-Real-Time RIC (Non-RT RIC). The Non-RT RIC is a componentof the SMO (Service Management and Orchestration) framework. It operates17Figure 2.4: RAN Intelligent Controller (RIC) overview [2].at control loop intervals greater than 1 second and focuses on long-termoptimization and orchestration tasks. It provides:• Policy-based guidance• ML model management• Enrichment information for the Near-RT RICThis RIC hosts rApps, which are logic applications designed to supportRAN automation, strategic decision making, and lifecycle management [24].xApps and rApps: The inclusion of custom applications—xApps in theNear-RT RIC and rApps in the Non-RT RIC—is one of the defining features18of the O-RAN architecture. These applications allow for tailored solutionsbased on specific network requirements and enable third-party innovation inthe RAN space.xApps and rApps can:• Accelerate innovation in ML and AI• Improve RAN agility and flexibility• Enable rapid integration of new use cases• Open the ecosystem to specialized third-party developersBy decoupling the intelligence from the base RAN protocol stack and ex-posing standardized APIs, O-RAN ensures that RICs are not only powerful,but also adaptable for future technologies and deployments [22, 24].2.2.5 O-RAN InterfacesOne of the fundamental principles of the O-RAN architecture is the adop-tion of open, standardized interfaces that facilitate seamless interoperabilitybetween components developed by different vendors. This architectural flexi-bility enables network operators to customize deployments according to theirspecific performance and cost requirements, promoting a more diverse andinnovation-driven vendor ecosystem [2]. Instead of relying on vertically in-tegrated RAN solutions, vendors can now specialize in developing modular,best-in-class components that integrate through well-defined interface stan-dards [22].The O-RAN specification defines the following interfaces for enablingcommunication between major architectural components:19Interface Between Used forO1 SMOO-RAN networkfunctionsNear-RT RICEnables SMO to perform FCAPS (Fault,Configuration, Accounting, Performance,Security) for O-RAN functions (excludingO-RU) and Near-RT RICO2 SMOO-CloudAllows SMO to manage and orchestrateinfrastructure and workloads hosted onthe O-CloudOpenFron-thaulM-PlaneSMOO-RUEnables SMO to perform FCAPS func-tions of the O-RUE2 Near-RT RICO-RAN networkfunctionsEnables the Near-RT RIC to collecttelemetry and issue control actions to theO-RAN functionsA1 Non-RT RICNear-RT RICAllows Non-RT RIC to send policies, en-richment data, and ML models to Near-RT RIC and receive feedbackTable 2.1: O-RAN Interfaces and Their Functions2.2.6 O-RAN Functional SplitThe O-RAN FS refers to dividing the network’s processing functions be-tween the CU, DU, and RU. Each split determines the allocation of protocolstack tasks across these units. This modularization enhances flexibility indeploying RAN architectures and enables dynamic allocation of computingresources [3]. Figure 2.5 provides a visual overview of the standardized splitoptions and the corresponding layer boundaries among the CU, DU, and RUentities.It is important to note that the FS options (1–8) shown in Fig. 2.5 are de-fined by 3GPP (TR 38.801) for new generation RAN and do not directly fol-low the classical seven-layer OSI model. Instead, they represent split pointswithin the RAN protocol stack. However, a rough correspondence can be20drawn: Option 1 (RRC/PDCP) aligns with the higher OSI layers (applica-tion/session), Options 2–4 (PDCP–MAC) map to the data link/transportfunctionality, and Options 5–8 progressively descend into the physical layerand RF domain. In particular, Option 7.2, adopted by the O-RAN Alliancefor the open fronthaul interface, corresponds to an intra-PHY split separat-ing PHY-high functions (e.g., coding, modulation) at the DU from PHY-lowfunctions (e.g., Fast Fourier Transform (FFT)/Inverse Fast Fourier Trans-form (IFFT) and RF processing) at the RU [25].Figure 2.5: O-RAN Functional Split [3]Split Option 1: IP Layer Processing In Split Option 1, the CU isresponsible for all higher-layer processing, including the PDCP, RLC, and21MAC layers. The RU handles only the physical layer tasks. This setupcentralizes most processing within the CU, making it suitable for scenarioswhere latency is not a primary concern.The CU manages all layers up to the MAC, while the RU exclusively takescharge of the physical layer. This division allows for centralized managementof higher-layer protocols, while the RU handles the more localized physicallayer operations.Processing Steps:• Data Packet Reception: Data packets arrive at the CU and areprocessed through the PDCP layer, which handles tasks such as headercompression, ciphering, and integrity protection.• RLC Layer Processing: The RLC layer at the CU manages segmen-tation and reassembly, error correction, and ARQ (Automatic RepeatRequest).• MAC Layer Scheduling: The MAC layer at the CU handles schedul-ing, multiplexing, and prioritization of data for transmission.• Physical Layer Processing: The processed data is forwarded to theRU, where the physical layer takes over for modulation, coding, andRF transmission.This split is ideal for Fixed Wireless Access (FWA) and other non-mobileapplications where bandwidth efficiency and relaxed latency requirements areprioritized. It is also beneficial in scenarios where the network infrastructurecan accommodate centralized processing.Split Option 2: PDCP to RLC Split This FS divides processing re-sponsibilities between the PDCP layer, managed by the CU, and the RLCand MAC layers, handled by DU. This option balances centralized controlwith distributed processing, allowing for more flexible and efficient networkmanagement.The CU retains control over the PDCP layer, which performs tasks suchas header compression and ciphering. Meanwhile, the DU manages the RLC22and MAC layers, focusing on segmentation, error correction, and real-timescheduling.Processing Steps:• PDCP Layer: The PDCP layer at the CU handles ciphering, integrityprotection, and header compression before passing the data to the DU.• RLC Layer Segmentation: At the DU, the RLC layer managessegmentation, reassembly, and error correction, ensuring data integritybefore scheduling.• MAC Layer Scheduling: The MAC layer at the DU is responsiblefor scheduling and prioritizing data for transmission over the physicallayer.• Physical Layer Transmission: The data is then passed to the RUfor physical layer modulation, coding, and RF transmission.This split is well-suited for mobile applications where a balance betweenlatency and centralized management is required. It supports flexible deploy-ment scenarios, particularly in networks that benefit from centralized PDCPprocessing while enabling distributed real-time control.Split Option 3: High RLC to Low RLC Split In Split Option 3, theRLC layer is divided between High RLC, managed by the CU, and Low RLC,handled by the DU. This FS enables the CU to centralize error correction andretransmission management, while the DU focuses on lower-layer operationssuch as segmentation and scheduling.The CU is responsible for High RLC tasks, including error correction andretransmission logic. The DU processes the Low RLC layer, which handlessegmentation, reassembly, and error detection, along with the MAC layer forreal-time scheduling.Processing Steps:• High RLC Processing: The CU processes data through the HighRLC layer, managing error correction and retransmissions to maintaindata integrity.23• Low RLC Segmentation: The DU handles Low RLC operations,such as segmentation, reassembly, and packet-level error detection andcorrection.• MAC Layer: The DU’s MAC layer schedules the prepared data fortransmission, managing priorities and transmission timing.• Physical Layer: The data is passed to the RU for modulation, en-coding, and over-the-air RF transmission.This split is ideal for use cases where centralized control of retransmissionlogic is essential, such as in mobile data networks where maintaining highreliability and integrity across variable conditions is critical.Split Option 4: MAC Layer Division In Split Option 4, the MAClayer is divided between high-level functions, managed by the CU, and low-level tasks, handled by DU. This configuration offers fine-grained controlover MAC-layer functionalities, which is essential for latency-sensitive andtime-critical applications.The CU is responsible for high-level MAC operations, including schedul-ing, Hybrid Automatic Repeat Request (HARQ), and flow control. Mean-while, the DU processes low-level MAC functions such as multiplexing andMAC header management before forwarding the data to the RU.Processing Steps:• High-Level MAC Scheduling: The CU schedules data packets andmanages HARQ to ensure efficient use of transmission resources andreliable data delivery.• Low-Level MAC Multiplexing: The DU handles data multiplex-ing and MAC header generation, preparing the packets for lower-layertransmission.• Physical Layer: The DU forwards the processed data to the RU,where it is subjected to physical layer operations such as modulationand coding.• RF Transmission: The RU transmits the final signal over the air viaRF transmission.24This split is suitable for real-time applications that demand precisescheduling control and low-latency performance. It supports use cases inmobile communication systems and other critical deployments requiring hightiming accuracy and optimized resource management.Split Option 5: MAC to PHY Split In Split Option 5, all functionsof the MAC layer are managed by the CU, while the DU is responsible forthe entire physical layer. This configuration significantly reduces latency bypushing time-sensitive processing closer to the radio interface.The CU handles MAC operations such as scheduling, HARQ, and flowcontrol. The DU takes over all physical layer tasks, including modulation,coding, and signal processing before data is sent to the RU.Processing Steps:• MAC Layer Processing: The CU schedules data packets, managesHARQ procedures, and performs flow control, ensuring reliable andefficient communication.• Physical Layer Processing: The DU handles all physical layer tasks,including modulation, channel coding, and preparing signals for RFtransmission.• RF Transmission: The RU transmits the prepared RF signal overthe air interface to the end-user device.This split is well-suited for Ultra-Reliable Low Latency Communication(URLLC) scenarios where delay minimization is critical. It also supportshigh-speed, low-latency applications that benefit from centralized MAC co-ordination combined with distributed physical-layer execution.Split Option 6: High PHY to Low PHY Split In Split Option 6, thephysical layer is divided into two components: High PHY and Low PHY. DUhandles High PHY tasks such as resource mapping, beamforming, and FFT,while the RU manages Low PHY operations, including modulation, coding,and signal conversion.25This configuration reduces the processing burden on the RU and enablesthe DU to take advantage of more centralized and capable computing re-sources. It also supports more efficient signal processing and resource coor-dination across distributed antennas.Processing Steps:• High PHY Processing: The DU performs High PHY operations,including resource element mapping, beamforming, and FFT compu-tations.• Low PHY Processing: The RU processes the received signal throughLow PHY layers, including modulation, coding, and digital-to-analogsignal conversion.• RF Transmission: The RU transmits the fully processed RF signalover the air to user equipment.This split is particularly suitable for high-bandwidth applications andmoderate-latency scenarios, such as those involving multiple antenna(MIMO) deployments. It supports improved resource utilization and effectivesignal chain division across RAN components.Split Option 7: Detailed Physical Layer Splits [3] Split Option 7introduces more granular divisions within the physical layer, enabling flexi-bility in assigning specific PHY tasks between the DU and RU. It includesthree sub-options: 7-1, 7-2, and 7-3, which vary based on how FFT, resourcemapping, precoding, and beamforming are distributed.Sub-option 7-1: FFT to Resource Mapping In this sub-option,the FFT is processed by the DU, while the RU handles resource mappingand remaining physical layer tasks. This setup simplifies RU design whilecentralizing critical frequency-domain operations in the DU.Processing Steps:• FFT Processing: The DU performs FFT to convert time-domainsignals to the frequency domain.26• Resource Mapping: The RU handles resource mapping, assigningfrequency resources for outgoing data streams.• Low PHY Processing: The RU performs modulation, channel cod-ing, and prepares the signal for radio transmission.• RF Transmission: The RF signal is transmitted by the RU over theair interface.This configuration is ideal for high-bandwidth applications that demandprecise frequency-domain processing, such as advanced MIMO systems.Sub-option 7-2: Resource Mapping to Precoding In this varia-tion, the DU manages resource mapping, while the RU takes over precodingand beamforming tasks. This setup provides fine-grained control over radiosignal shaping at the RU.Processing Steps:• Resource Mapping: The DU allocates frequency and time resourcesfor scheduled transmissions.• Precoding and Beamforming: The RU performs precoding to shapesignals for spatial multiplexing and beamforming to direct the energytoward specific users.• Low PHY Processing: The RU handles modulation, channel coding,and prepares signals for final transmission.• RF Transmission: The RU transmits the processed RF signal to theend-user equipment.This sub-option is well-suited for millimeter-wave (mmWave) applicationswhere accurate beam control is essential.27Sub-option 7-3: Precoding to Beamforming This sub-option di-vides precoding and beamforming between the DU and RU, respectively.Precoding is performed by the DU, while beamforming is executed at theRU, helping reduce fronthaul bandwidth while retaining spatial signal con-trol.Processing Steps:• Precoding: The DU performs signal precoding based on channel con-ditions and spatial requirements.• Beamforming: The RU applies beamforming techniques to guide thesignal toward specific users or locations.• Low PHY Processing: The RU manages modulation, coding, andsignal conditioning for RF.• RF Transmission: The RU transmits the beamformed RF signal tothe user terminal.This configuration is optimal for dense urban deployments with limitedfronthaul bandwidth, where flexible, localized beamforming is needed to en-sure robust communication.Split Option 8: RF to Low PHY Split Split Option 8 represents themost fundamental functional separation in the O-RAN architecture. In thisconfiguration, the RU is responsible solely for Radio Frequency (RF) signaltransmission and reception, while all other processing tasks—including theentire physical layer—are managed by DU.By offloading all digital baseband processing to the DU, this split drasti-cally simplifies the RU, reducing both hardware complexity and deploymentoverhead. The DU takes full control of physical layer functions such as mod-ulation, coding, and signal conditioning.Processing Steps:• Low PHY Processing: The DU handles all digital physical layertasks beyond RF, including channel coding, modulation, and signalpreparation.28• RF Signal Management: The RU focuses on RF-specific responsi-bilities, such as converting baseband signals to RF for transmission andvice versa for reception.• Signal Transmission: The RU transmits and receives the RF signalsover the air, acting as the final interface to user equipment.This split is especially suitable for large-scale deployments with manyantenna ports, where reducing RU complexity is critical to achieving scalable,cost-effective network infrastructure.The implementation of FS in the O-RAN architecture allows operatorsto customize the distribution of processing tasks across the CU, DU, andRU to meet specific deployment and performance requirements. Each splitoption offers a unique trade-off between centralization, latency, bandwidthefficiency, and RU complexity. Table 2.2 provides a summarized comparisonof all O-RAN FS options, highlighting their task distribution and typicaluse cases. This structured overview helps visualize how O-RAN enablesmodular, scalable, and cost-efficient network designs by decoupling hardwareand software functions across different architectural components.29Split Op-tionCU Tasks DU Tasks RU Tasks Typical Use CaseSplit 1 PDCP, RLC,MAC– PHY FWA, relaxed latency,centralized controlSplit 2 PDCP RLC, MAC PHY Balanced mobile de-ploymentsSplit 3 High RLC Low RLC, MAC PHY Centralized retrans-mission logicSplit 4 MAC (schedul-ing, HARQ)MAC (multi-plexing, head-ers)PHY Low-latency real-timecommsSplit 5 MAC PHY RF URLLC, low-latencyedge use casesSplit 6 – High PHY(FFT, mapping)Low PHY + RF MIMO deployments,moderate latencySplit 7.1 – FFT Mapping, LowPHY, RFHigh-bandwidth apps,frequency processingSplit 7.2 – Resource map-pingPrecoding,beamforming,RFmmWave beam steer-ing controlSplit 7.3 – Precoding Beamforming,RFUrban networks withfronthaul limitsSplit 8 – PHY RF onlyScalable, lowcomplexity RUdeploymentsTable 2.2: Summary of O-RAN Functional Split Options For Splits 6–8, mostprocessing responsibilities are handled by the DU and RU.2.3 Cell-Free Massive MIMO2.3.1 Introduction and Evolution of Wireless SystemsThe progression of wireless communication systems has been characterized bya continuous quest for higher data rates, lower latency, improved reliability,and better user fairness. First-generation (1G) networks were analog andfocused primarily on voice transmission. The advent of 2G and 3G introduceddigital communications and multimedia capabilities, while 4G Long TermEvolution (LTE) aimed at supporting mobile broadband with higher spectralefficiency (SE).30Figure 2.6: Traditional cellular network coverage showing lower data rate atthe cell edges [4].Despite advances in spectral efficiency (SE) through techniques suchas orthogonal frequency division multiplexing (OFDM) and multiple-inputmultiple-output (MIMO), traditional cellular networks continued to sufferfrom inherent limitations. These include strong inter-cell interference, highlynon-uniform user experiences—particularly at the cell edges where path lossis severe—and persistent challenges in load balancing [12]. The introductionof Massive MIMO in the 2010s represented a major breakthrough, as large-scale antenna arrays at base stations enabled spatial multiplexing of multipleusers, thereby improving both throughput and energy efficiency (EE). Nev-ertheless, Massive MIMO was still deployed within the confines of a cellulararchitecture, where user association and performance remained tied to geo-graphical cell boundaries [26].To overcome these limitations, the concept of CF-mMIMO emerged, fun-damentally rethinking the radio access architecture. Proposed formally byNgo,, Yang, Larsson, and Marzetta in 2015 [12], the cell-free architectureeliminates the concept of cells. Instead of associating users with a single BS,31a large number of distributed APs, often with one or a few antennas, collabo-ratively serve all active users in the network. In CF-mMIMO, the number ofAPs are equal or greater than active users. The transmission and receptionoccur over the same time-frequency resources, leading to macro-diversity,interference suppression, and a highly uniform service experience.This user-centric approach builds upon earlier concepts such as Coor-dinated Multi-Point (CoMP) transmission and network MIMO but extendsthem by integrating dense AP deployment with centralized coordination. Thetheoretical foundations of CF-mMIMO rely on channel hardening and favor-able propagation, properties that ensure stable link quality and low inter-userinterference as the number of APs increases [5].Figure 2.7: User-centric clustering in CF-mMIMO showing APs coordinatedby CPUs and serving overlapping UE groups [4].32Recent studies, such as those by Interdonato et al. [11] and Bjo¨rnson etal. [27], have demonstrated that CF-mMIMO systems can outperform bothtraditional macro-cellular networks and dense small-cell networks in termsof SE, EE, and user fairness. These advantages become especially prominentin scenarios with high user density, non-uniform traffic demand, and varyingpropagation conditions.Figure 2.8: Comparison of data rate distribution: unlike the cellular case(Fig. 2.6), CF-mMIMO provides nearly uniform throughput across the entireservice area.Figures 2.6–2.8 provide a visual comparison between the cellular and cell-free paradigms. In the traditional cellular model (Fig. 2.6), users close totheir serving BS experience high throughput, while those at the cell edgessuffer from poor performance due to path loss and strong inter-cell interfer-ence. The CF-mMIMO architecture (Fig. 2.7) removes the notion of fixedcell boundaries by allowing multiple distributed APs to jointly serve eachUE under centralized coordination. As a result, the throughput distributionbecomes highly uniform across the service area, as shown in Fig. 2.8, demon-strating one of the key advantages of CF-mMIMO over traditional cellularsystems [5].CF-mMIMO represents a paradigm shift in wireless network design, mov-ing away from the traditional notion of cells and dedicated BSs toward auser-centric, collaborative framework. In a Cell-Free architecture, a large33number of distributed APs, each equipped with one or a few antennas, serveall users within a coverage area simultaneously on the same time-frequencyresources. These APs are connected to one or more Central ProcessingUnit (CPU) via fronthaul links, enabling coordinated processing for bothuplink and downlink transmissions [12].The term cell-free emphasizes the absence of cell boundaries. Unlike tra-ditional cellular systems, where each user connects to a single BS based onreceived signal strength, CF-mMIMO allows each user to be dynamicallyserved by a subset of APs that are most favorable from a large-scale fad-ing perspective. This approach results in highly uniform service quality,improved fairness, and reduced inter-user interference.2.3.2 Architectural FrameworkThe architectural framework of CF-mMIMO fundamentally departs fromconventional cellular designs. Instead of dividing the coverage area into fixedcells served by high-power BSs, CF-mMIMO deploys a large number of low-power APs distributed throughout the network. These APs jointly serve allusers in a cooperative manner without rigid cell boundaries. This user-centricarchitecture improves macro-diversity, enhances link reliability, and providesa more uniform quality of service across the network area [12].All APs are connected to a CPU via fronthaul links. The CPU performscentralized tasks such as signal processing, user scheduling, power allocation,and coordination among APs. Depending on the network configuration, theCPU may also manage clustering strategies to reduce complexity and im-prove scalability. The performance of such systems depends critically onthe fronthaul infrastructure, which must support low-latency, high-capacitycommunication to ensure real-time coordination [5].Three primary architectural variants are widely discussed in the litera-ture:• Fully Centralized Architecture: In this setup, APs operate as sim-ple radio heads, forwarding raw or quantized baseband signals to theCPU, where all processing tasks are performed. This enables globallyoptimized coordination but imposes stringent requirements on fron-thaul bandwidth and synchronization [11].34• Distributed Processing Architecture: In this model, APs carryout more advanced local functions such as channel estimation, pre-coding, or full transmit/receive processing. Only final processed orcompressed data is sent to the CPU. This reduces fronthaul demandsbut increases the complexity of AP hardware [27].• Hybrid (Semi-Distributed) Architecture: This approach balancesthe above two extremes by splitting processing tasks between APs andthe CPU. It offers a practical compromise between coordination per-formance, system complexity, and fronthaul requirements [28].Another critical design element is the clustering mechanism. In user-centric clustering, each user is served by a subset of nearby APs selectedbased on large-scale channel statistics, such as path loss. This reduces com-putational load and fronthaul traffic while retaining most benefits of fullycooperative processing [29, 30].In summary, the architectural framework of CF-mMIMO is adaptable toa wide range of deployment scenarios, from dense indoor environments towide-area outdoor networks. Its flexibility and cooperative nature make it acompelling solution for future wireless systems that require high reliability,fairness, and SE.2.3.3 Channel Modeling and Fading EnvironmentsAccurate channel modeling is fundamental to the analysis, design, and op-timization of CF-mMIMO systems. Due to the distributed nature of thearchitecture, each user experiences unique propagation conditions with eachAP, necessitating a more detailed representation than in traditional cellularsystems.In Cell-Free networks, the channel between the AP l and user k is typi-cally modeled as a block-fading channel. The channel remains constant overa coherence block of τc symbols and changes independently across blocks.This assumption simplifies system analysis and reflects real-world scenariosin which channel variations occur more slowly than the symbol rate.35Mathematically, the small-scale fading channel between access point l anduser k is modeled ashlk ∼ CN (0, γlkI), (2.1)where hlk ∈ CN×1 is the channel vector from the N -antenna AP l to theuser k. The term γlk denotes the large-scale fading coefficient capturingpath loss and shadowing, and I is the N ×N identity matrix. The notationCN (0, γlkI) refers to a circularly symmetric complex Gaussian distributionwith zero mean and covariance matrix γlkI, modeling independent Rayleighfading across antennas. This channel model follows the standard block-fadingformulation widely adopted in Cell-Free Massive MIMO literature [5].It is important to note that the distance between AP l and user k does notappear explicitly in the small-scale fading model of (2.1). Instead, its effectis captured in the large-scale fading coefficient γlk, which incorporates pathloss—typically modeled as d−αlk , where dlk is the AP–UE distance and α isthe path-loss exponent—as well as log-normal shadowing. Thus, γlk providesthe distance-dependent scaling of the small-scale Rayleigh fading process.Large-scale fading coefficients γlk are assumed to remain constant overmultiple coherence intervals. This temporal stability enables their use incentralized or distributed algorithms for resource allocation and user schedul-ing. Accurate estimation of γlk is essential since it directly impacts clusteringstrategies, power control, and user association decisions [12].A key advantage of Cell-Free architectures is their exploitation of favor-able propagation—a condition where the wireless channels of different usersbecome asymptotically orthogonal as the number of APs grows. This prop-erty facilitates efficient multi-user separation using simple linear processingschemes such as MRC and ZF [26]. The near-orthogonality of user channelsminimizes inter-user interference and simplifies the derivation of closed-formexpressions for capacity and Signal-to-Interference-plus-Noise-Ratio(SINR).Another important property is channel hardening, where the effectivechannel gain concentrates around its mean as the number of cooperating APsincreases. This reduces reliance on instantaneous channel state information(CSI) and improves system robustness under dynamic conditions [5].36In practice, however, factors such as spatial correlation, line-of-sight(LoS) propagation, and hardware impairments introduce deviations fromideal Gaussian channel models. Studies such as [31, 32] have extended themodeling framework to include these non-idealities and shown that advancedsignal processing can mitigate associated performance degradation.Figure 2.9: Structure of a coherence block illustrating pilot and data trans-mission phases [5].Figure 2.9 illustrates the structure of a coherence block, which includesboth pilot training and data transmission phases. This structure determineshow time-frequency resources are allocated for channel estimation and datatransfer, directly influencing the system’s SE and overall throughput [5].To improve modeling accuracy, stochastic geometry-based frameworkshave been proposed to reflect the random spatial distribution of APs and UEsin practical deployments [11]. These models help in characterizing averagenetwork behavior across random topologies and are valuable for system-levelperformance evaluation at scale.In conclusion, CF-mMIMO channel modeling must balance analyticaltractability and real-world realism. While block-fading models with favorablepropagation and channel hardening remain dominant for analysis, incorpo-rating more sophisticated models is necessary to accurately reflect practicaldeployment scenarios.372.3.4 Uplink and Downlink TransmissionCF-mMIMO supports both uplink and downlink transmissions through thejoint service of multiple distributed APs. Spatial multiplexing gains areachieved via coherent combining on the uplink and coordinated beamformingon the downlink. Unlike traditional cellular systems, where each BS processesonly local signals, Cell-Free architectures enable AP collaboration across thenetwork, improving throughput and fairness for all users.Uplink TransmissionDuring uplink transmission, each UE transmits its signal to all APs overshared time-frequency resources. The received signal at AP l is modeled asyl =K∑k=1√pkhlksk + nl, (2.2)where pk is the transmit power of user k, hlk is the channel coefficient betweenAP l and user k, sk is the transmitted symbol of user k, and nl ∼ CN (0, σ2)represents additive complex Gaussian noise at AP m [5, Eq. 3.1].The APs forward their received signals—or sufficient statistics—to aCPU, where linear combining techniques are applied to detect the trans-mitted symbols. Common uplink combining strategies include:- Maximum Ratio Combining (MRC):vMRCk = hˆk, (2.3)where hˆk is the estimated channel vector from all APs to user k.- Zero-Forcing (ZF) Combining:VZF = (HˆHHˆ)−1HˆH , (2.4)- Minimum Mean Square Error (MMSE) Combining:VMMSE = (HˆHHˆ+ µI)−1HˆH , (2.5)where Hˆ ∈ CL×K is the estimated uplink channel matrix, and µ is a regular-ization parameter.38The APs forward their received signals—or sufficient statistics—to aCPU, where linear combining techniques such as Maximum Ratio Combin-ing (MRC), Zero-Forcing (ZF), and Minimum Mean Square Error (MMSE)combining are applied to detect the transmitted symbols. MRC uses theconjugate of the estimated channel vector for each user, providing a sim-ple and robust approach under favorable propagation conditions. However,MRC does not actively suppress inter-user interference. ZF improves detec-tion performance by nullifying interference through channel matrix inversion,although it requires accurate channel state information and may suffer in ill-conditioned scenarios. MMSE combining offers a balanced alternative byintroducing a regularization term that suppresses interference while control-ling noise amplification. This makes MMSE particularly effective in denseor interference-limited deployments, where MRC may fall short and ZF maybecome unstable [12, 30].Figure 2.10 illustrates this uplink signal flow from UEs to distributedAPs, and how the centralized processor applies combining to recover eachuser’s signal.Figure 2.10: Uplink transmission in CF-mMIMO showing signals from UEsto APs and centralized combining techniques.39Downlink TransmissionIn the downlink, the CPU generates precoded symbols and distributes themto the APs. Each AP transmits a weighted sum of symbols destined formultiple users:xl =K∑k=1√ρlkwlk qk, (2.6)where qk is the data symbol intended for user k, wlk is the precoding vectorapplied at access point l for user k, and ρlk is the allocated downlink transmitpower [5].Each AP sends a superposition of precoded symbols. Common downlinkprecoding strategies include:- Maximum Ratio Transmission (MRT):wlk = hˆ∗lk, (2.7)where hˆlk denotes the estimated channel from AP l to user k.- Zero-Forcing (ZF):WZF = HˆH(HˆHˆH)−1, (2.8)where Hˆ ∈ CL×K is the estimated global channel matrix, with each columnrepresenting the concatenated estimated channels from all APs to user k.- Minimum Mean Square Error (MMSE):WMMSE = HˆH(HˆHˆH + µI)−1, (2.9)where µ is a regularization parameter introduced to control noise amplifica-tion and improve numerical stability in the precoding process, and I is theidentity matrix of appropriate size.MRT is computationally simple and resilient to channel estimation errorsbut offers limited interference suppression due to its non-discriminative na-ture. ZF enhances interference mitigation by nullifying inter-user interferencethrough matrix inversion, although it requires precise channel state informa-tion and is sensitive to ill-conditioned channel matrices. MMSE precoding40strikes a balance between these extremes by incorporating regularization tosuppress interference while mitigating noise amplification, making it more ro-bust in practical CF-mMIMO deployments, especially under moderate SNRconditions.Figure 2.11 illustrates the downlink signal flow from the centralized pro-cessor to the UEs, showing how distributed APs transmit precoded symbolsusing linear precoding techniques.Figure 2.11: Downlink transmission in CF-mMIMO showing APs transmit-ting precoded symbols to UEs using linear precoding technique.A major challenge in the downlink is CSI acquisition at the user end. InTime Division Duplex (TDD) systems, downlink CSI is inferred from uplinkpilots using channel reciprocity. However, calibration is necessary to correcthardware asymmetries that break reciprocity assumptions.Recent research has explored joint user association and power control toimprove throughput and fairness. These techniques often leverage large-scalefading information for AP clustering, reducing computation and fronthaulusage. Since pilot overhead impacts overall system capacity, pilot contami-nation and reuse strategies are discussed in detail in Section 2.3.5.It is also worth noting that the choice of FS (e.g., FS 7.2 vs. FS 8)influences uplink and downlink fronthaul load. FS 8 involves forwarding raw41in-phase/quadrature (IQ) samples from the APs, leading to higher bandwidthrequirements. In contrast, FS 7.2 allows partial local baseband processing,thus reducing fronthaul traffic.In summary, uplink and downlink operations in CF-mMIMO are charac-terized by collaborative processing, soft AP clustering, and the eliminationof cell boundaries. These features result in high SE, better interference man-agement, and improved mobility support.Implementation Note: While this section presents various combiningand precoding strategies—such as MRC, ZF, MMSE, and MRT—for theoret-ical completeness, the remainder of this thesis specifically employs MMSEcombining for uplink detection andMMSE precoding for downlink trans-mission. This choice reflects the superior balance offered by MMSE betweeninterference suppression and noise robustness, making it well-suited for denseCF-mMIMO deployments.2.3.5 Channel Estimation and Pilot ContaminationChannel estimation is a foundational task in CF-mMIMO systems, as it di-rectly impacts the performance of uplink detection and downlink precoding.Reliable CSI enables coherent signal processing and efficient resource alloca-tion across distributed APs.Uplink Channel EstimationIn time-division duplexing (TDD) operation, users transmit pilot sequencesduring the uplink training phase. Each AP uses the received pilot signalsat BBU to estimate the corresponding user channels. Let τp denote thenumber of pilot symbols. The received pilot signal matrix at AP l, denotedby Yp,l ∈ CN×τp , contains the superimposed pilot transmissions from all Kusers over the N antennas of the AP and is given by:Yp,l =K∑k=1√pk hlkϕHk +Np,l, (2.10)where pk is the transmit power of user k, ϕk ∈ Cτp×1 is the pilot sequenceassigned to user k, hlk ∈ CN×1 is the channel vector between AP l and user42k, and Np,l ∈ CN×τp is the additive noise matrix. This model follows theformulation described in [5, Eq. 4.1].Assuming orthogonal pilots and known second-order statistics, the MMSEestimate of the channel between AP l and user k is:hˆlk =Yp,lϕk∥ϕk∥2 + σ2 (2.11)This estimator minimizes the expected squared error between the trueand estimated channels by leveraging known large-scale fading coefficientsand pilot sequences.Pilot ContaminationDue to the limited coherence interval τc, the number of orthogonal pilotsequences τp is constrained. In dense networks, it is often the case thatK > τp, which necessitates pilot reuse. When multiple users share the samepilot, APs cannot perfectly distinguish between them, resulting in pilot con-tamination. This contamination degrades both channel estimation accuracyand subsequent data transmission.Pilot contamination is one of the most significant challenges in Cell-Free systems. Studies have shown that even under favorable propaga-tion conditions, pilot reuse causes SINR degradation and worsens user fair-ness—especially in the downlink [12, 5].Several mitigation strategies have been proposed to combat pilot contam-ination:• Pilot Assignment Algorithms: Assign pilots such that users withhigh mutual interference receive orthogonal sequences. Greedy andheuristic algorithms are commonly employed [33].• Spatial Separation Using Large-Scale Fading: Use the large-scalefading coefficients γlk to avoid assigning the same pilot to users withoverlapping coverage areas [27].• Pilot Power Control: Adjust pilot transmission power adaptively toreduce interference among users sharing the same pilot.43• Pilot Decontamination Techniques: Methods such as subspaceprojection, blind estimation, and deep learning models are used to inferthe dominant eigen-directions for cleaner CSI extraction [30, 32].User scheduling also contributes to mitigation. Users with correlatedspatial locations or similar γlk profiles can be scheduled in different time orfrequency resources. Since TDD-based downlink precoding relies on uplinkchannel estimates, contamination propagates from the uplink to the downlinkunless reciprocity calibration is performed [5, Ch. 4].Furthermore, distributed estimation schemes have been explored to re-duce training overhead. In [32], joint pilot assignment and AP-user clusteringare employed to minimize training duration while maintaining high SE.In summary, while MMSE-based estimation provides optimal channel re-covery under ideal conditions, real-world deployments require intelligent pilotreuse strategies, power control, and adaptive clustering to combat pilot con-tamination and enable robust channel estimation in CF-mMIMO networks.2.3.6 Spectral Efficiency and Power ControlSpectral efficiency (SE) is a fundamental performance metric used to eval-uate the effectiveness of wireless communication systems. It quantifies howefficiently the available spectrum is used to transmit data and is typically ex-pressed in bits per second per Hertz (bps/Hz). In the context of CF-mMIMOnetworks, SE plays a critical role in system design and optimization.In Cell-Free networks, the achievable SE for each user depends on theSINR, which is influenced by the channel conditions, pilot contamination,and the adopted detection or precoding method. A general expression forthe uplink SE of user k is [5]:SEk =(1− τpτc)log2(1 + SINRk), (2.12)where τp is the number of pilot symbols, τc is the length of the coherenceinterval, and SINRk is the effective signal-to-interference-plus-noise ratio foruser k. In this thesis, MMSE combining is employed for both uplink anddownlink analysis, as it provides superior interference suppression comparedto simpler schemes such as MR combining.44Power ControlPower control is a critical aspect of CF-mMIMO design, aimed at balancingtrade-offs among fairness, throughput, and EE. Common objectives for powercontrol include:• Max-Min Fairness: Maximize the minimum SE across all users toensure uniform service quality [12].• Sum-Rate Maximization: Allocate transmit power to maximize to-tal network throughput.• EE Optimization: Maximize the SE per unit of consumed energy [30,34].These objectives are typically formulated as convex optimization prob-lems and solved using methods such as geometric programming, dual decom-position, or iterative algorithms. SINR constraints are often reformulated assecond-order cone constraints and addressed using standard convex solvers.In practical deployments, power control is usually performed using large-scale fading coefficients γlk, which remain stable over longer timescales. Thiseliminates the need for real-time exchange of instantaneous CSI and enablesscalable optimization [27]. Dynamic power control algorithms have also beenproposed, where both APs and UEs iteratively adjust their power levels basedon local feedback [28].Learning-Based Power Control: Recent advancements have intro-duced machine learning (ML) methods for power control. In [33], deep re-inforcement learning was employed to learn power allocation policies fromchannel and QoS data, outperforming traditional heuristics. A decentral-ized approach was presented in [35], enabling individual APs to make powerdecisions without requiring full network CSI.Simulation studies consistently show that optimized power control signifi-cantly improves SE in Cell-Free architectures, particularly under user-centricclustering. Coordinated power profiles from multiple APs reduce inter-userinterference and enhance effective SINR [5, Ch. 5].In conclusion, power control remains a vital mechanism in CF-mMIMOsystems. It not only enhances SE but also addresses fairness, energy usage,45and scalability constraints. A wide spectrum of solutions—from convex op-timization to decentralized machine learning—are available to tailor powercontrol strategies to specific system goals.2.3.7 Scalability and DecentralizationAs CF-mMIMO systems scale to encompass hundreds of APs and users, en-suring real-time operability and computational tractability becomes a majorchallenge. While centralized processing can provide theoretical performancebounds, it introduces significant computational and fronthaul burdens inwide-area deployments. This section explores decentralized and user-centricmethods proposed in recent research to make large-scale Cell-Free implemen-tations practical.Centralized vs. Decentralized ProcessingIn fully centralized architectures, all APs forward their received signalsand estimated CSI to a CPU, which performs detection, precoding, andpower control. While this model guarantees global coordination, it is lim-ited by fronthaul bandwidth, processing delays, and synchronization require-ments—particularly when the number of APs grows beyond several dozen [5].To address these challenges, decentralized architectures have been intro-duced. In such designs, subsets of APs perform local operations—such aschannel estimation, combining, or precoding—and only partially coordinatewith the CPU. For instance, in [11], APs form dynamic clusters around usersin their vicinity to reduce global coordination overhead while still benefitingfrom cooperative processing.User-Centric Clustering and Local ProcessingA prominent decentralization strategy is user-centric clustering, in whicheach user is served by a selected subset of nearby APs based on large-scalefading coefficients. These clusters can be static or dynamic, with the latteradapting to user mobility and channel variability [31]. User-centric cluster-ing significantly reduces fronthaul signaling and computation while retainingmost of the performance benefits of centralized coordination.46Message Passing and Local AlgorithmsDistributed detection and precoding can be implemented through message-passing algorithms, including consensus-based updates and belief propaga-tion. These methods enable APs to iteratively exchange summaries of localinformation to approximate globally optimal decisions. In [28], a hybrid ap-proach was proposed where APs perform a limited number of ZF iterationslocally before sending compressed results to the CPU.Pilot Assignment and Scheduling in Decentralized SettingsAs network scale increases, scalable pilot assignment becomes crucial. Localpilot coordination strategies can minimize pilot collisions by reusing pilotsacross spatially distant APs. Reference [36] introduced structured pilot reuseand massive access schemes that reduce training overhead while preservingCSI quality in large systems.Distributed Power Control and LearningDistributed power control further supports scalability by allowing each APor user to adjust its transmission power using only local interference andchannel data. In [35], a reinforcement learning-based decentralized powercontrol algorithm was proposed that achieves near-optimal performance withminimal coordination.Fronthaul-Aware OptimizationTo mitigate fronthaul constraints, several fronthaul-aware strategies havebeen explored. These include selective quantization, traffic prioritization,and flexible function splitting. Such techniques ensure that only relevantinformation is transmitted across the fronthaul, which is essential for inte-gration with O-RAN platforms and operation over heterogeneous backhaultechnologies [37].ConclusionIn conclusion, scalable CF-mMIMO operation relies on decentralizing corefunctions such as channel estimation, detection, scheduling, and power con-trol. User-centric processing, message-passing algorithms, and distributed47machine learning techniques serve as essential enablers. These methods areindispensable for achieving practical, large-scale deployments of Cell-Freesystems, particularly in dense and wide-area wireless networks.2.3.8 Energy Efficiency ModelsEE is a crucial performance metric for sustainable wireless networks, espe-cially in the context of large-scale CF-mMIMO deployments. EE is typicallydefined as the ratio of total system throughput to the total power consump-tion, measured in bits per Joule. Unlike traditional systems that often tradeperformance for energy savings, Cell-Free architectures aim to achieve bothby distributing the energy load across a dense set of low-power APs.The EE of the network is defined asEE =∑k RkPtotal, (2.13)where Rk is the achievable rate of user k, and Ptotal represents the totalsystem-wide power consumption, which includes transmission power at APs,circuit power for RF chains and baseband units, power used in fronthaul andbackhaul communication, and static power for site operation [5].EE vs. SE Trade-offsMaximizing EE does not necessarily coincide with maximizing SE. For exam-ple, higher transmit power improves SE but increases energy usage, therebylowering EE. This trade-off requires joint optimization of throughput andpower consumption. Reference [27] explores this by formulating multi-objective optimization problems that balance SE and EE under various sys-tem constraints.EE Optimization StrategiesSeveral strategies have been proposed to improve EE in CF-mMIMO systems:• Sleep Mode Scheduling: Temporarily deactivate APs not servingusers during a coherence block to save static and dynamic power [33].• Load-Aware Clustering: Dynamically reassign APs based on userload to avoid over-serving users with poor SINR [28].48• User-Centric Power Allocation: Allocate power based on user de-mand, location, and QoS priorities.• Green Transmission Policies: Apply only the minimum necessarypower to meet QoS constraints.Centralized and Distributed ApproachesEE optimization can be carried out either centrally at the CPU or throughdecentralized mechanisms:• In [35], a decentralized reinforcement learning approach is introducedwhere each AP adjusts its transmit power and on/off decisions basedon local utility.• In [32], joint user association and power control are optimized to reducetotal energy consumption while satisfying SINR constraints.These problems are often non-convex and solved using techniques such asfractional programming, sequential convex approximation, or dual decompo-sition.Fronthaul and Infrastructure ConsiderationsFronthaul energy consumption is a significant factor in overall EE modeling.High-capacity fronthaul technologies like fiber optics can consume substantialstatic and dynamic power, particularly when transporting full IQ samples.To address this, fronthaul-aware clustering, data compression, and hybridprecoding have been proposed [37]. Figure 2.12 provides a visual breakdownof the various components contributing to total power consumption in CF-mMIMO systems, including transmission, fronthaul, and static site power.49Figure 2.12: Breakdown of total power consumption components in CF-mMIMO systems.ConclusionEE in CF-mMIMO is influenced by transmission policies, hardware configu-rations, clustering mechanisms, and fronthaul architecture. Advanced opti-mization frameworks and intelligent control strategies, ranging from convexoptimization to machine learning are essential for achieving dual goals of sus-tainable operation and high-performance service in future wireless networks.2.3.9 Integration with O-RANThe integration of CF-mMIMO with the O-RAN architecture represents apromising direction to realize scalable, intelligent, and energy-efficient wire-less systems. While CF-mMIMO redefines the radio access paradigm byremoving the concept of cells and introducing user-centric cooperation, O-RAN supports open, disaggregated, and programmable RAN architecturesthat enhance interoperability and deployment flexibility.50Architectural SynergyO-RAN is structured around a disaggregated model consisting of O-RU, O-DU, O-CU, and the RIC. This modular approach aligns closely with the func-tional blocks of CF-mMIMO. In Cell-Free systems, access APs—equivalent toO-RUs—are distributed across the coverage area and coordinated by a CPU,which performs functions analogous to those of the O-DU and O-CU [37].The O-RAN support for flexible FS (e.g., FS 7.2 and FS 8) enables differ-ent distributions of baseband processing tasks in RU, DU and CU. In FS 7.2,lower PHY-layer functions—such as FFT, IFFT and resource mapping, areoffloaded to the RU, while higher PHY and upper-layer functions remain inthe DU and CU. In contrast, FS 8 centralizes the entire PHY and higher-layer baseband stack within the DU/CU, leaving the RU as a pure radiofrequency (RF) front-end. In both cases, the fronthaul interface betweenthe O-RU and O-DU typically employs the enhanced Common Public RadioInterface (eCPRI), a packet-based protocol for transporting digitized radiosignals over Ethernet. In CF-mMIMO systems, a CPU is typically responsi-ble for coordinating a group of distributed APs. This CPU handles key signalprocessing tasks such as data decoding, precoding, and resource allocation.When mapping to the O-RAN architecture, this coordinating entity can beviewed as the equivalent of a DU. In practice, a DU in O-RAN can serve acluster of APs, making it functionally similar to the CPU in CF-mMIMO.This architectural alignment enables CF-mMIMO systems to take advantageof O-RAN’s flexible deployment model while maintaining their centralizedprocessing nature. Figure 2.13 illustrates the integration of CF-mMIMOwithin the O-RAN framework, showing the mapping between CF-mMIMOcontrol units and O-RAN functional blocks.51Figure 2.13: Integration architecture of CF-mMIMO within the O-RANframework [6].RIC-Enabled IntelligenceO-RAN introduces two types of RICs: Near-RT-RIC and Non-RT-RIC.These components support the deployment of xApps and rApps for intel-ligent RAN control. This control paradigm is highly beneficial for Cell-Freenetworks, where centralized intelligence is needed to manage coordination,clustering, and resource optimization.For example, a Near-RT-RIC can host xApps that dynamically adjustAP-user associations based on real-time traffic and interference measure-ments. Simultaneously, the Non-RT-RIC can run rApps that implementlong-term control policies such as load balancing, mobility prediction, andenergy-saving strategies [22]. These features enable Cell-Free deploymentsto be more responsive, energy-aware, and adaptable to network conditions.52Fronthaul and Synchronization AlignmentO-RAN supports flexible fronthaul standards, such as eCPRI, and standard-ized synchronization protocols. These features can be leveraged by CF-mMIMO to reduce reliance on high-capacity dedicated links and to ensuretime and frequency alignment across distributed APs. FS such as 7.2 and8 offer additional flexibility by enabling selective distribution of processingtasks between APs and processing units. With intelligent scheduling andclustering, Cell-Free deployments can effectively utilize shared O-RAN trans-port and fronthaul infrastructure [37, 22]. Figure 2.14 maps the FS (7.2and 8) to the corresponding uplink and downlink signal processing chains inthe O-RAN architecture.Figure 2.14: Functional splits in O-RAN architecture (7.2 and 8) mapped touplink and downlink processing chains.Virtualization and Edge ComputingThe cloud-native and virtualization-friendly nature of O-RAN makes it par-ticularly suitable for deploying Cell-Free control units. Virtualized Network53Functions (VNFs) and container-based deployments can host centralizedCell-Free processing modules at edge cloud nodes. This allows for low-latencycoordination and processing, which are essential for real-time joint transmis-sion and reception. Furthermore, edge computing platforms can supportscalable AI models that manage distributed transmission policies and per-form dynamic clustering in Cell-Free systems [5].Challenges in IntegrationDespite strong architectural alignment, several technical challenges must beaddressed for full integration:• Cell-Based Origins: O-RAN was initially designed with traditionalcell-based networks in mind. Supporting user-centric Cell-Free connec-tivity requires modifications to existing RAN architecture and proto-cols.• RIC Adaptation: RIC applications (xApps/rApps) need to be re-designed or extended to support dynamic user-centric clustering anddecentralized coordination.• Hardware Heterogeneity: Vendor diversity in hardware and soft-ware interfaces may introduce integration complexity and synchroniza-tion issues in large-scale, dense AP deployments.• Standardization Gaps: Current O-RAN specifications do not yet de-fine explicit procedures or APIs for Cell-Free RAN coordination, mak-ing implementation fragmented and vendor-specific.Nonetheless, research prototypes and emulation platforms—such as thosebased on OpenAirInterface and srsRAN—are beginning to explore Cell-Freeconcepts over O-RAN-compliant testbeds [37].ConclusionCF-mMIMO and O-RAN are highly complementary technologies. The mod-ular, open, and programmable nature of O-RAN provides a flexible platformfor deploying the real-time, distributed control required by Cell-Free archi-tectures. In return, the performance, fairness, and robustness of Cell-FreeMIMO can significantly enhance O-RAN-based networks. Together, they54form a foundational pillar for scalable, intelligent, and energy-efficient 6GRANs.2.3.10 Summary of CF-mMIMO LiteratureThis section consolidates key findings from the literature on CF-mMIMO,emphasizing its theoretical foundations and the technological advancementsthat have shaped its role as a leading candidate for future wireless accessnetworks.The foundational contributions by Ngo et al. [12] and Demir et al. [5]laid the groundwork for CF-mMIMO theory, establishing the following corebenefits:• Uniform service quality across the entire coverage area,• Enhanced macro-diversity via spatially distributed APs,• Interference suppression enabled by favorable propagation and channelhardening,• Improved spectral and EE through joint signal processing.To achieve these gains, advanced signal processing techniques such asMRC, ZF, and MMSE precoding have been extensively studied. In addition,user-centric clustering, distributed estimation, message-passing algorithms,and machine learning-based network control have been introduced to improvescalability and operational adaptability in large-scale deployments [30, 35,33].EE and pilot contamination remain prominent themes in the CF-mMIMOliterature. Several promising strategies have been proposed:• Pilot assignment and spatial reuse policies to reduce contamination,• Eigenvalue-based pilot decontamination and blind estimation,• Adaptive power control leveraging large-scale fading statistics [27, 32,29].55Recent studies also underscore the growing synergy between CF-mMIMOand the O-RAN paradigm. O-RAN’s support for modular and disaggregatedarchitectures, RICs, and flexible FS aligns well with the user-centric anddistributed nature of Cell-Free deployments [37, 36].In summary, the literature presents CF-mMIMO as a robust and adapt-able solution for next-generation networks. Its strong theoretical under-pinnings, evolving signal processing techniques, and compatibility with O-RAN infrastructure make it a promising architecture for achieving high-performance, scalable, and energy-efficient wireless systems.2.4 Research GapWhile the preceding sections reviewed the theoretical foundations and tech-nological advancements of O-RAN and CF-mMIMO, several important re-search gaps remain. Most existing studies examine these technologies inisolation, with limited work on their integrated deployment under realisticconstraints. In particular, factors such as fronthaul capacity, edge processinglimitations, and imperfect CSI are often overlooked, and the performanceimplications of different FS—especially FS 7.2 and FS 8—have not beencomprehensively analyzed within a Cell-Free MIMO context.The following research gaps are identified:1. A notable research gap is the lack of comprehensive simulation stud-ies that investigate O-RAN FS in CF-mMIMO systems under diversescalability scenarios, specifically considering different access point (AP)densities and antenna configurations per AP.2. Absence of performance benchmarking across SE, EE, and fairness met-rics within an O-RAN-based CF-mMIMO environment.3. Limited investigation into the architectural trade-offs between central-ized (Split 8) and partially centralized (Split 7.2) setups in terms ofprocessing complexity, fronthaul requirements, and system scalability.4. Insufficient modeling of performance degradation in decentralized CF-mMIMO deployments, particularly in relation to interference (α) andchannel gain (β) reduction parameters.562.5 Research ContributionBased on the identified gaps, this thesis makes the following contributionstoward evaluating and improving the integration of CF-mMIMO into O-RANarchitectures:1. Enhanced a simulation framework to support CF-mMIMO evaluationunder O-RAN FS (FS 7.2 and FS 8), introducing flexibility for varyingantenna configurations and scalable AP deployments.2. Evaluation of system-level performance metrics—including SE, EE, andJFI—across multiple antenna configurations and distributed AP sce-narios.3. Quantitative modeling and comparison of centralized versus decentral-ized architectures, factoring in processing capacity differences and fron-thaul demands.4. Introduction of empirical degradation parameters α and β to captureinterference amplification and channel gain reductions in decentralizedCF-mMIMO deployments.5. Comparative analysis across architecture types, offering design insightsfor energy-efficient and fairness-aware O-RAN deployments aimed atnext generation wireless networks.ConclusionThis chapter has reviewed two foundational pillars of next-generation wirelesssystems: the O-RAN) and CF-mMIMO. The discussion highlighted thearchitectural modularity and open interfaces introduced by O-RAN, whichpromote interoperability and vendor neutrality, alongside the distributed,user-centric design of CF-mMIMO, which addresses limitations in SE, fair-ness, and cell-edge performance.While both technologies offer scalable and efficient solutions individually,their integration presents new trade-offs involving coordination overhead,57fronthaul bandwidth constraints, synchronization complexity, and the distri-bution of processing tasks. These issues are especially critical in the contextof FS architectures such as Split 7.2 and Split 8.The chapter also identified key research gaps in the existing literature,particularly the limited evaluation of CF-mMIMO performance under differ-ent O-RAN FS and realistic deployment constraints. This thesis addressesthose gaps through a simulation-based performance analysis framework, aim-ing to contribute practical insights for the design of open, scalable, andenergy-efficient wireless access networks.The following chapter introduces the system model and simulation envi-ronment developed to evaluate these architectural trade-offs.58Chapter 3Effect of Functional Split onthe Integrated Cell-FreemMIMO with O-RANThis chapter presents the complete system model and simulation frameworkused to evaluate CF-mMIMO architectures integrated with O-RAN princi-ples. The modeling framework, adapted from [5], incorporates key enhance-ments in AP density, antenna configurations, fronthaul constraints, and FSstrategies. It includes detailed formulations of the wireless channel model,fronthaul capacity modeling, and architectural configurations aligned withO-RAN FS 7.2 and FS 8. Additionally, the chapter outlines the problem for-mulation for joint power and resource optimization, and introduces the pro-posed optimization algorithms used for simulation. The system is analyzedunder both centralized (O-RAN compliant) and decentralized deploymentscenarios. This holistic framework enables a rigorous performance assess-ment across key metrics, including SE, EE, and user fairness.3.1 Architectural Configurations of CF-mMIMO Under O-RAN FrameworkThis section describes the general configuration of the CF-mMIMO net-work under investigation. The network consists of L distributed APs, eachequipped with N antennas, serving K users randomly located in an A km2coverage area. APs are deployed uniformly throughout the service region59and are connected to a CPU or operate autonomously, depending on the de-ployment scenario. TDD mode is used, and all APs operate over a commonfrequency band of B MHz bandwidth. The system assumes block-fadingchannels with perfect time synchronization and orthogonal pilot assignment.For the CF-mMIMO system we considered three architectural configura-tions, corresponding to different levels of centralization and O-RAN FS: fullycentralized (FS 8), partially centralized (FS 7.2), and fully decentralized pro-cessing.Fully Centralized (FS 8):In the fully centralized architecture (aligned with O-RAN Split 8), all APsfunction as O-RUs (Radio Units) and are connected via high-capacity fron-thaul links to a CPU, which embodies the O-DU/O-CU combination. Thisunit performs all baseband processing tasks—including channel estimation,precoding, decoding, power control, and user scheduling—using global CSI.This configuration enables full coordination and network-wide optimizationbut imposes high fronthaul bandwidth and centralized computing demands.Figure 3.1: Cell-Free mMIMO with O-RAN FS 8 Deployment60Partially Centralized (FS 7.2):The partially centralized architecture corresponds to O-RAN Split 7.2. Inthis setup, physical layer functionalities are distributed between the APs(O-RUs) and a mid-level processing unit (O-DU). While some baseband pro-cessing (e.g., FFT/IFFT, partial precoding) is performed at the AP level,higher-layer operations such as full precoding, decoding, and scheduling arehandled at the central unit. This architecture strikes a balance between coor-dination and fronthaul load, enabling moderate centralization with reducedbackhaul requirements compared to FS 8.Figure 3.2: Cell-Free mMIMO with O-RAN FS 7.2 DeploymentDecentralized Deployment:The third configuration is fully decentralized architecture, which does notconform to a conventional O-RAN setup or with any CPU. Here, each APoperates independently, without fronthaul connectivity to a CPU. All signalprocessing—including channel estimation, local precoding, and decoding—isperformed locally using only local CSI. This eliminates the need for central-ized control and significantly reduces infrastructure complexity and fronthaulusage. However, it limits coordination across the network, potentially result-ing in degraded performance in interference-dense environments.61Figure 3.3: Decentralized Deployment without O-RANTo realistically model the impact of decentralization, this study intro-duces two empirical degradation parameters:• α: captures the increased interference due to lack of global coordina-tion,• β: reflects the reduction in effective channel gain resulting from relianceon local CSI.These parameters are applied to evaluate the performance gap betweencentralized, partially centralized, and fully decentralized deployments acrossSE, EE, and fairness metrics. This tri-architecture framework allows for acomprehensive analysis of the trade-offs involved in CF-mMIMO deploymentunder different O-RAN-aligned and non-O-RAN system models. A summarycomparison of the key features of these three architectures is provided inTable 3.1.62Feature Fully Central-ized (FS 8)Partially Cen-tralized (FS7.2)Fully Decen-tralizedO-RAN Align-mentO-RU + central-ized O-DU/O-CUunit (FS 8)O-RU with O-DU/O-CU (Split7.2)Independent ofO-RAN standardProcessing Loca-tionAll processing atCPUSplit processingbetween AP andcentral unitFully local pro-cessing at APsCSI Availability Global CSI avail-able at CPUPartial CSI avail-able at both APand CPUOnly local CSIavailableCoordination Ca-pabilityFull network-widecoordinationthrough central-ized unitCoordinationthrough bothcentralized unitand APsOnly local coor-dination throughAPsFronthaul Re-quirementHigh (trans-mission ofraw/basebandIQ samples)Moderate (trans-mission of par-tially processeddata)NoneComputationalLoadHigh (central-ized)Moderate (sharedbetween APs andCPU)Low per AP (fullydistributed)Flexibility andScalabilityLimited (due tofronthaul andCPU bottlenecks)Moderate (clus-tering improvesscalability)High (easily scal-able but limitedcoordination)Best Use Cases High-performanceurban networkswith strong in-frastructureSuburban or mid-scale deploymentsrequiring coordi-nationRural orinfrastructure-constrainedscenariosTable 3.1: Comparison of CF-mMIMO Architectural Configurations63As seen in Table 3.1, FS 8 maximizes coordination and global CSI usagebut requires significant fronthaul and centralized computing resources. FS 7.2offers a practical compromise between performance and fronthaul efficiency,while the fully decentralized setup eliminates fronthaul overhead but suffersfrom reduced coordination and higher interference.3.2 Channel ModelThe wireless channel between the AP l and the UE k is modeled as a Rayleighfading channel with large-scale fading coefficients. The channel is assumedto remain constant over a coherence block of τc symbols, which is standardfor time-division duplex (TDD) systems. Due to channel reciprocity in TDD,the base station estimates both uplink and downlink channels using uplinkpilot signals transmitted by the users. These assumptions and modelingtechniques are well-established in foundational CF-mMIMO literature [12, 5].The Uplink and Downlink data transmission models are described next.3.2.1 UplinkDuring the uplink phase, all K users transmit data symbols simultaneouslyover shared time-frequency resources. The signal received at AP l is expressedasyl =K∑k=1√pk hlk sk + nl, (3.1)where yl is the signal received in AP l, pk is the transmission power of UEk, hlk is the complex channel coefficient between AP l and UE k, sk is thesymbol transmitted by UE k and nl ∼ CN (0, σ2) is additive Gaussian whitenoise in AP l.3.2.2 DownlinkIn the downlink, APs employ coordinated transmission using linear precodingto jointly serve all UEs. The signal transmitted from AP l is given by:xl =K∑k=1√plk wlk qk, (3.2)64where xl is the transmit signal from AP l, plk is the power control coefficientallocated to UE k at AP l, wlk is the precoding weight applied at AP l forUE k, and qk is the intended data symbol for UE k.Equations (3.1) and (3.2) provide the foundation for signal modeling inCF-mMIMO and will be used throughout the subsequent analysis of SINR,achievable rates and power allocation schemes. The uplink and downlinktransmission models, along with the the corresponding SINR formulations,follow the standard CF-mMIMO framework presented in [5].3.3 Functional Splits and Fronthaul Model-ingThis study evaluates two O-RAN FS options—FS 7.2 and FS 8—which definehow baseband processing is distributed between Access Points (O-RUs) andcentralized units (O-DUs/O-CUs). These splits directly influence coordina-tion capabilities, latency, and fronthaul bandwidth requirements [10, 38, 39].In Split 7.2 (partially centralized), lower physical (PHY) layer functionssuch as FFT/IFFT and partial resource mapping are offloaded to the O-RU.This significantly reduces the required fronthaul bandwidth but restricts fullcentralized coordination, particularly for interference mitigation and jointtransmission.In Split 8 (fully centralized), all PHY processing is performed at thecentral unit, and the O-RUs function as passive radio heads that simplyrelay I/Q samples. This enables network-wide coordination, such as jointbeamforming and centralized precoding, but at the cost of significantly higherfronthaul data rate.Following the fronthaul modeling framework from [39, 38, 40], the fron-thaul data rate per AP is expressed as:For Split 7.2:R(7.2)FH =2 ·Nbits ·Nused ·NTs, (3.3)65For Split 8:R(8)FH = 2 · fs ·Nbits ·N, (3.4)where N is the number of antennas per AP, Nused is the number of usedOFDM subcarriers, fs is the sampling frequency, Ts is the OFDM symbolduration, and Nbits is the number of quantization bits per sample. The factor2 accounts for the in-phase and quadrature (I/Q) components of the base-band signal. These expressions allow precise estimation of fronthaul require-ments and highlight the substantial increase in fronthaul burden introducedby Split 8 compared to Split 7.2.These equations form the basis for quantifying fronthaul scalability limi-tations in Cell-Free mMIMO networks and motivate further optimization infunction placement and transport resource allocation.A detailed numerical assessment comparing the number of APs that canbe supported per DU under fixed fronthaul capacity constraints for bothSplit 7.2 and Split 8 is provided in Appendix A3.4 Problem FormulationTo evaluate performance trade-offs in CF-mMIMO deployments within O-RAN architectures, we propose a general optimization framework that jointlyconsiders power allocation and AP activation. The objective is to achieve abalance between SE and total transmit power under practical constraints.Objective FunctionThe primary objective is to jointly optimizes the transmit power allocationacross all APs and the AP activation state to minimize the total networkpower consumption under minimum SE constraints for each user. Specif-ically, the joint optimization refers to two design variables: the transmitpower allocation vector plk across all AP-user pairs, and the AP activationvector zl, which determines whether each AP is turned on or off. This trade-off between EE and SE is controlled using a weighting parameter λ. Theoptimization problem is formulated as66min{plk,zl}Ptotal(plk, zl)− λ ·K∑k=1(τc − τpτc)log2 (1 + SINRk(plk, zl)) , (3.5)Subject toplk ≥ 0, for all l, k∑Kk=1 plk ≤ zl · Pmax, for all lzl ∈ {0, 1}, ∀lSINRk(plk, zl) ≥ SINRmin, for all k,where plk denotes the power allocation from AP l to user k, and zl is a re-laxed binary indicator for the ON/OFF status of AP l. Ptotal(plk, zl) denotesthe total power consumption, including transmission and infrastructure com-ponents. λ is a weighting parameter balancing power efficiency against SE.τc and τp are the coherence block length and pilot sequence length, respec-tively. Pmax is the maximum transmit power per AP. SINRk is the signal-to-interference-plus-noise ratio for user k, which must exceed a thresholdSINRmin. K and L denote the number of users and APs, respectively.In the objective function presented in Equation (3.5), two primary com-ponents govern the optimization strategy: the total power consumption,Ptotal(plk, zl), and the system-wide SE, represented as a weighted sum overall users. Specifically, the SE term aggregates the individual user rates SEkacross all K users, reflecting the total data throughput of the system. Thisaggregation captures the combined transmission capacity of the network andserves as a counterbalance to energy expenditure in the optimization trade-off. Together, these two components reflect the inherent tension between EEand throughput maximization in the design of CF-mMIMO systems operat-ing under O-RAN functional constraints.The weighting parameter λ in the objective function serves as a mech-anism to represent varying levels of user priority. Specifically, λ allows thenetwork operator to tune the optimization toward EE, SE or a balance ofboth depending on the operational context.• Under low-priority or low-load conditions, the optimization empha-sizes minimizing power consumption while still maintaining acceptable67SE.• In medium-priority cases, the objective function balances EE anddata rate performance by assigning moderate weight to the SE term.• For high-priority scenarios—such as peak demand or performance-critical services—the power constraint can be relaxed. In this case, theobjective reduces to maximizing aggregate user throughput by effec-tively prioritizing SE.Mathematically, this high-priority regime corresponds to the limiting casewhere λ→∞, and the objective function simplifies to:max{plk,zl}K∑k=1(τc − τpτc)log2 (1 + SINRk(plk, zl)) (3.5a)This variant reflects a performance-maximizing formulation where powerconsumption is not penalized. It models scenarios where achieving high SEtakes precedence over energy constraints. The subsequent sections providedetailed formulations and interpretations of each component.Total Power Consumption PtotalThe total network power consumption in a CF-mMIMO system under anO-RAN framework includes contributions from data transmission, hardwarecircuitry, fronthaul operation, and cooling requirements, following the layeredpower modeling principles proposed in [41] and tailored for CF-mMIMOsystem assumptions based on [5]. Based on the simulation-aligned model,the total power consumption is defined asPtotal(plk, zl) = Pfixed(zl) + Pload(plk, zl), (3.6)where p ∈ RKL×1 is the stacked power allocation vector defined as p =[p1,1, p1,2, . . . , pL,K ]T, with plk representing the power allocated from AP l touser k. and z ∈ [0, 1]L is a relaxed binary vector indicating the ON/OFFstatus of each AP. The total power comprises two key components:68Fixed Power ComponentThe fixed power component Pfixed(zl) is expressed asPfixed(zl) = PRU(zl) + PDU(zl) + Pfronthaul(zl) + Pcool-fixed(zl), (3.7)where PRU(zl) accounts for the static circuit and baseband power consumedat the radio unit (RU) of each active AP, weighted by zl. The term PDU(zl)represents the power associated with digital units (DUs), capturing signalprocessing and backhaul interfacing. Pfronthaul(zl) includes the power requiredto operate optical line terminals (OLTs), optical network units (ONUs), andassociated fronthaul transceivers. Lastly, Pcool-fixed(zl) models the energy con-sumed for cooling all active infrastructure, including RU, DU, and fronthaulsubsystems, normalized by the cooling efficiency factor σcool.Load-Dependent Power ComponentThe load-dependent power component Pload(plk, zl) captures thetransmission-related energy from active APs and the dynamic process-ing load induced by user data streams. It can be defined asPload(plk, zl) =L∑l=1zl · ∥pl∥22 +K∑k=1(Pproc +∆proc) ·XLPkGOPSmax · σcool , (3.8)where ∥pl∥22 =∑Kk=1 p2lk denotes the total downlink transmission power fromAP l, computed as the squared Euclidean norm of its per-user power al-locations. This term is scaled by the activation status indicator zl, whichdetermines whether AP l is active (zl = 1) or inactive (zl = 0). The termXLPk represents the processing workload associated with user k, reflectingthe required computational effort for baseband processing. The second sum-mation accounts for the cumulative energy consumed by the baseband signalprocessing units across all users, normalized by the system’s available com-putational capacity GOPSmax and the cooling system efficiency σcool. Here,Pproc and ∆proc denote the static and dynamic components of baseband pro-cessor power consumption, respectively.The structure and parameterization of the power model follow the frame-works presented in [41] and are adapted for CF-mMIMO deployments asdiscussed in [5].69Spectral EfficiencyIn the SE formulation, the pre-log factor(τc−τpτc)represents the fractionof the coherence block available for data transmission after pilot signaling.Since both the coherence block length τc and the pilot sequence length τpare assumed to be fixed for a given carrier frequency and system bandwidth,this term remains constant throughout the optimization process. Therefore,in the following derivations, the focus is placed on the logarithmic termlog2(1+SINRk), which captures the dynamic contribution of power allocationand interference management to the user-specific SE.The SINR for user k is computed as a function of the AP-to-user powerallocations plk and the AP activation indicators zl. The SINR expression isexpanded asSINRk(plk, zl) =∣∣∣∑Ll=1 bl,k · zl · plk∣∣∣2∑Ki=1i ̸=k∣∣∣∑Ll=1 cl,k,i · zl · pli∣∣∣2 + σ2 , (3.9)where bl,k denotes the effective scalar channel gain from AP l to user k,capturing the coherent signal strength. The coefficients cl,k,i represent theinterference coupling between the signal transmitted by AP l to user i, asreceived by user k, and are derived from second-order statistics (i.e., thevariance of hHl,kwl,i). The numerator models the coherent desired signal powerfor user k, while the denominator accumulates the interference contributionsfrom all other users (i ̸= k) and adds the receiver noise power σ2.Effective Channel Vector bk in CF-mMIMOIn the context of a CF-mMIMO system, the vector bk ∈ CL×1 representsthe collective channel gains between all APs and user k, weighted by thepower allocated from each AP to that user. Specifically, bk is constructed bystacking the scalar products of each AP-to-user channel coefficient and theassociated transmit power.Mathematically, the scalar contribution of AP l to user k is defined as[bk]l = E[hHl,kwl,k], (3.10)70where hl,k ∈ CN×1 denotes the small-scale fading channel vector between APl and user k, and wl,k ∈ CN×1 represents the beamforming vector appliedat AP l for user k. The superscript H refers to the Hermitian (conjugatetranspose) operation, and the expectation is taken to account for statisticalchannel variations.The full vector bk is expressed asbk =E[hH1,kw1,k]E[hH2,kw2,k]...E[hHL,kwL,k] ∈ CL×1 (3.11)This compact representation captures the coherent signal components re-ceived by user k from all L APs and is used to compute the numerator of theSINR expression in Equation (3.9). Each entry contributes constructively to-ward the desired signal strength at user k, assuming perfect phase alignmentthrough beamforming.In the SINR expression of Equation (3.9), the scalar entries bl,k from thevector bk are used directly to compute the coherent signal power receivedfrom each AP. This scalar-based representation simplifies the SINR calcula-tion by making the AP-wise contributions explicit.Interference Coupling Coefficient cl,k,iThe scalar quantity cl,k,i ∈ C represents the interference coupling from thesignal transmitted by AP l to user i, as it is received by user k. This termcaptures the second-order channel statistics, including cross-correlation andleakage between the users. It is used in the SINR denominator to quantifythe cumulative interference at user k from all other users i ̸= k.In the SINR expression (Equation 3.4), the full interference power is com-puted by summing the squared contributions of cl,k,i · zl · pli across all APsand users.71Power Allocation Vector pThe variable p ∈ RKL×1 represents the vector of effective transmit amplitudesfrom all APs to all users. Each element plk denotes the power allocated byAP l to user k. In optimization, plk is treated as a scalar design variable, re-stricted to be nonnegative and bounded by the maximum allowable transmitpower per AP.The vector p is formed by stacking plk values across all users k = 1, . . . , Kand all APs l = 1, . . . , L, typically in column-major order. This variableis central to both power consumption and SE, and its optimal allocationgoverns the system performance under centralized or decentralized processingconstraints.While the full power allocation strategy can be represented as a stackedvector p ∈ RKL×1, the SINR expression in Equation (3.9) uses the individualscalar terms plk directly to reflect per-AP, per-user power contributions in amore granular form.Noise Power σ2The term σ2 represents the power of additive white Gaussian noise at thereceiver of user k. It is treated as a known system constant and computedasσ2 = N0 ·B, (3.12)where N0 is the noise power spectral density and B is the system bandwidth.This noise power is added in the SINR denominator to represent the receiver’sthermal noise floor.In practice, the receiver noise power also depends on the hardware noisefigure F , such that σ2 = N0 · B · F . In this study, we assume F = 1 (idealreceiver) and treat σ2 = N0 ·B as the effective thermal noise floor, consistentwith standard CF-mMIMO analyses [12, 5].In the subsequent simulation framework, power control is formulated asan optimization problem aimed at minimizing total transmission power while72satisfying minimum SE constraints per user. The resulting non-convex prob-lem is solved using a CCP-based iterative approach, as described in [5].3.5 Proposed AlgorithmThe optimization framework formulated in Section 3.4 aims to jointly mini-mize the total network power consumption while ensuring a guaranteed SEthreshold for each user in a CF-mMIMO system operating under O-RAN FSconstraints. As shown in Equation 3.5, the problem is non-convex due tothe presence of logarithmic terms involving SINRk(plk, zl) and the bilinearcoupling between power allocation variables plk and AP activation indicatorszl in the constraints.The formulated problem cannot be solved analytically due to its inherentnon-convexity. Specifically, the SINR expressions contain both non-convexterms and binary activation variables, leading to a mixed-integer non-linearprogram that is computationally intractable for direct analytical solutions.Conventional closed-form beamforming techniques, such as zero-forcing (ZF),are designed to cancel interference but often fail to achieve optimal sum-rateperformance in practical multi-user CF-mMIMO scenarios [42]. Iterativemethods like the Weighted Minimum Mean Square Error (WMMSE) algo-rithm can improve performance but generally converge only to stationarypoints rather than the global optimum [43]. Consequently, specialized it-erative convex approximation frameworks are required, as they can reliablyhandle interference coupling, activation decisions, and the scale of realisticmulti-user networks—motivating the adoption of the Concave-Convex Pro-cedure (CCP) described in the following section.While a naive convexification strategy—such as replacing log2(1+SINRk)with a concave surrogate like − log2(1/SINRk)—may simplify the structure,it is overly conservative and fails to capture the multi-user interference andsignal coupling intrinsic to CF-mMIMO systems. Consequently, this thesisadopts a more principled strategy by leveraging the Concave-Convex Pro-cedure (CCP), a well-established iterative method for solving Difference-of-Convex (DC) programs [5].The CCP algorithm linearizes non-convex terms—such as log2(1+SINRk)and interference expressions—around the current solution at each iteration.73This results in a sequence of convex subproblems that can be efficiently solvedusing convex solvers such as CVX [44]. The updated solution from eachiteration is then used as the linearization point for the next, and the processcontinues until convergence criteria are met.While CCP provides high-quality solutions, it often activates a large num-ber of weak AP-user links with negligible power, which increases both com-putational overhead and implementation complexity. To mitigate this, arefinement stage is introduced to prune low-power links and encourage spar-sity in the solution. This stage applies a predefined threshold to eliminatepower allocations below a certain level and resolves the convexified problemwith those links fixed to zero, ensuring minimal SE degradation.Together, these two stages—CCP-based convex approximation and sparserefinement—constitute a two-level optimization framework that ensures fea-sibility, EE, and practical deployability across various O-RAN FS. The com-plete algorithmic process is detailed in Subsections 3.5.1 and 3.5.2.3.5.1 CCP-Based Iterative OptimizationCCP works by iteratively linearizing the non-convex components of the prob-lem around a current estimate, solving a convexified subproblem, and updat-ing the variables until convergence. In this context, SINR expressions andfronthaul capacity constraints are approximated using first-order Taylor ex-pansions.74Algorithm 1 CCP-Based Optimization Procedure1: Input: Initial solution (p(0), z(0)), tolerance ε, maximum iterationsMmax, set m← 02: repeat3: Construct convex approximations of non-convex terms:4: (a) Linearize log2(1 + SINRk) around current p(m)5: (b) Approximate interference and fronthaul terms if applicable6: Formulate convexified problem:7: Minimize Ptotal(p, z)− λ∑k SEk(p)8: Subject to:9: plk ≥ 0 ∀(l, k)10: ∥pl∥2 ≤ zl ·√Pmax ∀l11: zl ∈ {0, 1}, ∀l12: SINRk ≥ SINRmin ∀k13: Solve the convex problem and obtain (p(m+1), z(m+1))14: if ∥p(m+1) − p(m)∥ ≤ ε then15: break and return final solution (p∗, z∗)16: end if17: Update m← m+ 118: until m =Mmax19: Output: Optimized power allocation vector p∗ and activation vector z∗The Concave-Convex Procedure (CCP) adopted in this thesis addressesthe nonconvex optimization problem defined in Equation (3.5) by iterativelylinearizing nonconvex components through Taylor first-order approximations.In particular, the logarithmic SE term, log2(1 + SINRk(plk, zl)), which de-pends on the scalar power coefficients plk, is linearized with respect to thefull power vector p at each iteration. Similarly, interference expressions andbilinear coupling terms involving both power allocation variables plk and APactivation indicators zl are approximated using convex surrogates.At each iteration m, a convexified subproblem is formulated based on thelinearized terms and solved using a convex optimization solver such as CVX.The resulting updated solution (p(m+1), z(m+1)) is used as the new expansionpoint for the next iteration. This iterative process continues until either thesolution change between consecutive steps satisfies the convergence criterion∥p(m+1)−p(m)∥ ≤ ε, or the maximum number of iterations Mmax is reached.75In the simulation implementation, the channel gains bl,k and interferencecoefficients cl,k,i used in the SINR expression are derived from expectation val-ues based on MMSE channel estimation across multiple realizations. Thesecoefficients are embedded into the SINR expressions to reflect both AP-userlink strength and multi-user interference under varying power allocations andAP activation conditions. The CCP framework thereby transforms the origi-nal non-convex joint power and activation problem into a tractable sequenceof convex programs, providing efficient and scalable convergence to a locallyoptimal solution while meeting user-level SE and power constraints.3.5.2 Refinement Stage via Sparse Power Vector Re-coveryAlthough the CCP stage provides a feasible power allocation, it often resultsin numerous low-power links being active. These links contribute marginallyto system performance but introduce additional computational and signalingoverhead. To address this, a refinement step is introduced to enforce sparsityand enhance the scalability of the solution for practical deployment.76Algorithm 2 Refinement via Sparse Projection1: Input: Initial solution p∗ obtained from CCP2: Define sparsity threshold θ > 03: for each AP-user pair (l, k) do4: if p∗lk < θ then5: Mark link (l, k) as inactive6: Set plk ← 07: end if8: end for9: Convex Re-optimization with Inactive Links:10: Fix plk = 0 for all pruned links11: Solve the convex subproblem:12: Minimize Ptotal(p, z)− λ∑k SEk(p)13: Subject to:14: plk ≥ 0 for all (l, k)15: ∥pl∥2 ≤ zl ·√Pmax for all l16: zl ∈ {0, 1} for all l17: SINRk ≥ SINRmin for all k18: Output: Refined sparse solution (p˜, z˜)The refinement procedure operates by imposing a sparsity threshold θ =0.001×max(p), below which any AP-user transmission link (l, k) is deemedinsignificant and is consequently pruned by setting plk = 0. This step re-duces the number of active links, simplifying hardware implementation andreducing fronthaul burden. After pruning, the optimization problem is re-solved under the new sparsity constraints using convex programming. Theinactive links remain fixed at zero, and the remaining variables are updatedto recover feasibility while retaining as much SE performance as possible.The final sparse solution is denoted as (p˜, z˜), where the tilde signifies thatthe solution has been sparsified and re-optimized through link pruning. Thisfinal sparse solution strikes a balance between EE, algorithmic complexity,and real-world applicability in CF-mMIMO deployments under O-RAN FSarchitectures.773.6.3 ConclusionThe proposed two-stage optimization framework, comprising the CCP fol-lowed by a refinement stage through the projection of sparse power, wasimplemented in MATLAB using the CVX optimization toolbox. Withineach CCP iteration, linear approximations of non-convex components suchas SINR and fronthaul constraints are substituted to construct tractable con-vex subproblems. Upon convergence, the refinement stage is invoked to prunelow-power AP-user links, resulting in a sparse power allocation matrix thataligns with hardware constraints and practical deployment feasibility.This simulation-aligned implementation supports scalable optimizationacross various O-RAN architectures, including fully centralized FS 8, par-tially centralized FS 7.2 and fully decentralized configurations. By decouplingfeasibility from complexity, the proposed method enables energy-efficient op-eration while maintaining user-specific SE guarantees, addressing the coretrade-offs in CF-mMIMO systems operating under heterogeneous FS con-straints.78Chapter 4Simulation Setup, Results andDiscussion4.1 Simulation SetupThis section describes the simulation framework developed to evaluate theperformance of CF-mMIMO under three architectural configurations alignedwith different levels of O-RAN compliance: Fully Centralized (FS 8), Par-tially Centralized (FS 7.2), and Fully Decentralized. Each configuration issimulated using a network of K = 16 users, variable AP counts ranging fromL = 20 to 50 (in increments of 2), and antenna configurations N = 4 andN = 8. The simulations assume a carrier frequency of 2.6 GHz and a 20MHz bandwidth.To capture realistic deployment behavior, user and AP locations are ran-domly initialized within a 1 km2 coverage area. Each simulation is averagedover 30 distinct network layouts, with each layout undergoing 500 indepen-dent channel realizations to reflect small-scale fading variability. A TDDprotocol is used with block-fading channels and perfect synchronization.The key architectural setups are described below.4.1.1 Fully Centralized Architecture (FS 8)This setup corresponds to the O-RAN FS 8, where all signal process-ing tasks—including channel estimation, precoding, and decoding—are per-79formed at a centralized unit (O-DU/O-CU). The APs (O-RUs) act as radiofront-ends and are connected via high-speed fronthaul links (10 Gbps with 4antennas and 20 Gbps with 8 antennas) to a centralized processing unit with180 GOPS capacity. Global CSI is assumed to be available at the centralprocessor.4.1.2 Partially Centralized Architecture (FS 7.2)In this configuration, baseband processing is split between the APs (O-RUs)and the O-DU. FFT, partial precoding, and local channel estimation areperformed at the APs, while decoding, scheduling, and some coordinationfunctions are handled by the O-DU. The system uses moderate fronthaulcapacity (10 Gbps with both 4 and 8 antennas) and assumes partial CSIavailability at both APs and the DU. This model offers a trade-off betweencoordination capability and fronthaul complexity.4.1.3 Fully Decentralized Architecture (Non-O-RAN)This setup assumes no centralized processing or fronthaul links. Each APperforms local channel estimation, precoding, and decoding independentlyusing local CSI. APs are provisioned with 50 GOPS compute capacity. Thelack of coordination leads to potential performance degradation, modeledthrough empirical penalty parameters: α = 1.3 to represent increased inter-ference and β = 0.8 to model reduced effective channel gain, as suggested in[5], [4].4.2 Spectral Efficiency (SE) TrendsThis section analyzes the SE performance under different architectural con-figurations. SE is measured as the total network throughput per unit band-width (in bps/Hz), averaged across multiple deployment realizations. Thesimulations consider variations in the number of APs L = 20 to 50, antennaconfigurations N = 4 and N = 8, and two FS—FS 7.2 and FS 8—alignedwith the O-RAN standard.All results are obtained through Monte Carlo simulations implemented inMATLAB, which model realistic small-scale fading and power consumption80constraints. Monte Carlo methods are employed since closed-form perfor-mance expressions for SE, EE, and fairness become analytically intractableunder multi-user interference, fronthaul constraints, and power control. Byaveraging results over a large number of random channel realizations anduser deployments, the simulation provides statistically reliable estimates ofergodic performance. This approach is consistent with standard practices inthe CF-mMIMO literature (e.g., [12, 5, 27]).The analysis incorporates differentiated user priority levels classified asweights Maximum, Medium, and Low. These weights affect the scheduler’sbehavior, prioritizing users with higher weights to improve their throughputand service quality. Although this often leads to increased power expenditure,SE values generally improve with increasing user weights, reflecting effectiveresource utilization.Figure 4.1: SE vs. AP count (L) for antennas N = 4.Figure 4.1 shows SE performance for N = 4 antennas across a range ofAP counts L, comparing the centralized FS 8 and partially centralized FS 7.2configurations. The fully centralized FS 8 architecture exhibits consistentlyhigher SE due to its joint precoding capabilities and global CSI availabil-ity. As the AP count increases, the system benefits not only from enhancedmacro-diversity and tighter coordination, but also from improved coveragesince users are, on average, closer to one or more APs. This reduces path81loss and improves SINR, which directly translates into higher achievable SEaccording to Shannon’s capacity theorem.Furthermore, SE scales positively with user weights in both architectures.However, FS 8 shows more pronounced gains, indicating its superior abilityto adapt to priority-based scheduling. This can be attributed to FS 8’s fullycentralized processing, which allows for joint precoding and user schedulingdecisions to be made with access to global CSI (Channel State Informa-tion). Unlike FS 7.2, where only partial PHY processing occurs at the RUand decisions are more distributed, FS 8 enables tighter coordination amongAPs and better interference management. The centralized baseband unitin FS 8 can exploit channel correlations across the entire network, performmore effective power control, and optimize beamforming vectors jointly forall users, thus achieving higher multi-user multiplexing gains and spectralefficiency. These results underscore FS 8’s scalability and responsiveness todynamic user requirements in high-density deployments, particularly whensophisticated scheduling and precoding strategies are feasible.Figure 4.2: SE vs. AP count (L) for antennas N = 8.Figure 4.2 shows the spectral efficiency (SE) trends for N = 8 antennasper AP compared to the N = 4 case. Increasing the number of antennasfrom 4 to 8 significantly enhances SE for both FS, with FS 8 continuing tooutperform FS 7.2 across all load scenarios. The improvement in SE with82more antennas stems from two key factors. First, additional antennas enablehigher spatial multiplexing, allowing simultaneous transmission to more userswithin the same time-frequency resources. Second, the array gain from addi-tional antennas improves signal quality through more focused beamforming,which increases link reliability and reduces interference. These effects areparticularly impactful in centralized deployments such as FS 8, where jointprocessing across access points maximizes the spatial degrees of freedom.Furthermore, the prioritization weights amplify this trend: under high-loadconditions, the additional antenna resources significantly enhance through-put, whereas low-load scenarios benefit from improved link robustness andscheduling flexibility. Overall, the results confirm that increasing antennacounts provides clear spectral scalability benefits, especially when combinedwith centralized coordination in FS 8.However, scaling from N = 4 to N = 8 antennas per AP also introducesimportant implementation trade-offs. Larger antenna arrays increase hard-ware cost and RF-chain complexity (LNAs, mixers, ADC/DACs) as well asphysical form-factor constraints for compact AP deployments [31]. More-over, doubling the antenna count raises baseband processing workload andpower consumption, which increases cooling requirements and may reducesystem-level energy efficiency unless hardware and algorithms are carefullyco-optimized [5]. On the network side, centralized architectures must trans-port proportionally more CSI, user data, and precoder/combiner updates,thereby increasing fronthaul bitrate and synchronization demands. Thesefronthaul-related impacts are explicitly discussed in 3GPP specifications andstudy items on NR system descriptions and next-generation access require-ments [45, 46]. Consequently, while higher antenna counts offer clear spectralscalability, real-world deployments must balance the SE gains against hard-ware cost, energy consumption, and fronthaul transport constraints.A common trend observed for both N = 4 and N = 8 antenna config-urations is that the spectral efficiency of FS 7.2 approaches that of FS 8under medium and high priority weights. In these load regimes, the sys-tem becomes interference–limited rather than coordination–limited, mean-ing that the partial CSI available in FS 7.2 is already sufficient to supportnear-optimal scheduling and beamforming. As a result, FS 8 shows onlya modest advantage at medium load and an almost negligible gap at highload. In contrast, at low load conditions the difference is more pronounced,83since centralized joint processing in FS 8 can exploit global CSI to improveSINR and scheduling flexibility, providing a clear SE benefit over FS 7.2.It should be emphasized, however, that even when SE values of FS 7.2 andFS 8 are nearly equal at medium and high load, FS 7.2 incurs additionaldistributed processing and fronthaul signaling overhead. This translates intohigher power consumption per transmitted bit, an aspect that becomes evi-dent in the subsequent analysis of energy efficiency.Figure 4.3: SE comparison: Centralized (FS 8) vs. Decentralized for antennaN = 8.Figure 4.3 compares centralized FS 8 versus its decentralized counterpartfor N = 8 antennas. Although both centralized FS 8 and decentralizedsetups see SE improvements with increasing user weight, centralized FS 8still significantly outperforms the decentralized alternative. This is due tothe former’s ability to perform joint processing and CSI-based scheduling,which scale better under high-priority user demands.The performance degradation in decentralized is modeled using interfer-ence factor α = 1.3 and channel gain factor β = 0.8, capturing the impactof partial CSI and limited coordination. The decentralized system shows84approximately 15–20% lower SE, quantifying the coordination loss due tolocal-only channel information. In this context, α and β are penalty fac-tors introduced to model the impact of decentralization on SE. Specifically,α = 1.3 reflects reduced beamforming gain due to partial CSI available atDUs, while β = 0.8 accounts for coordination inefficiencies in decentralizedscheduling and resource allocation. These factors are applied as scaling termsin the SINR expression, effectively reducing the achievable SE in decentral-ized FS 8 compared to its centralized counterpart.4.3 Energy Efficiency (EE) TrendsEE quantifies the number of bits successfully transmitted per unit of totalpower consumed, expressed in bits per Joule. It captures both the spectralutilization efficiency and the energy cost of network operation. Mathemati-cally, it is defined as:EE =RsumPtotal[bps/Joule], (4.1)where Ptotal is the aggregate power consumption, comprising transmissionpower, baseband processing, and fronthaul energy components. The totalsystem throughput Rsum is computed from the aggregate SE and the availablesystem bandwidth (BW):Rsum = SEtotal × BW, (4.2)where BW is the bandwidth of the simulation setup (20 MHz) and SEtotalrepresents the overall SE summed across all users. This formulation ensuresthat EE reflects both spectral performance and energy consumption, allowingcomparison of different FS and antenna configurations.This section presents a comparative analysis of EE under varying systemconfigurations, including different AP counts, antenna setups (N = 4, 8),and three levels of user priority weighting: Low, Medium, and Maximum.These priority weights influence how resources are allocated, with higherpriority leading to greater emphasis on maximizing SE and user through-put—typically at the cost of increased power usage.Higher user priority settings, such as the Maximum weight, result inelevated baseband computation and transmission power due to aggressive85resource allocation for throughput optimization. Conversely, lower priorityweights conserve energy by relaxing QoS requirements, albeit at the cost ofunderutilizing available spectral resources.Figure 4.4: EE vs. AP (L) for antenna N = 4.Figure 4.4 illustrates the energy efficiency (EE) trends for N = 4 antennasper AP as a function of the number of APs (L) under different traffic weightscenarios. It is observed that FS 8 consistently achieves higher EE comparedto FS 7.2 across all AP counts and load conditions. This improvement is pri-marily attributed to FS 8’s centralized processing architecture, which enablesefficient joint beamforming, power control, and interference coordination. Bycentralizing baseband processing, FS 8 reduces redundant computations andmitigates inter-AP interference more effectively than FS 7.2, leading to im-proved energy utilization per successfully transmitted bit.However, the EE trends exhibit different behaviors depending on the traf-fic load conditions. For maximum load, EE decreases as the number of APsincreases, and the rate of decrease is more pronounced. This is becausehigh traffic weights prioritize throughput and stringent QoS requirements,which necessitate higher transmit power and more intensive signal process-ing, particularly as more APs are activated. The additional coordination and86processing overhead in large AP deployments amplify the total power con-sumption, resulting in a steep reduction in EE. For medium load scenarios,the EE also shows a decreasing trend with AP count, but the rate of decline isless severe because the traffic load and corresponding processing demands aremoderate. In contrast, for low-load conditions, EE improves with the numberof APs. This is attributed to the effective distribution of user traffic acrossmore APs, which reduces per-AP power requirements and improves energyutilization efficiency. Despite these differences, FS 8 consistently retains itsenergy advantage over FS 7.2, although the gap narrows under higher loadconditions due to increased centralized processing and fronthaul overhead.Figure 4.5: EE vs. AP (L) for antennas N = 8.Figure 4.5 shows the EE trends forN = 8 antennas per AP across differenttraffic weight scenarios. FS 8 consistently achieves higher EE than FS 7.2for all AP counts and load conditions, similar to the trend observed forN = 4. However, the gap between FS 8 and FS 7.2 is relatively smaller in theN = 8 case, indicating that the energy advantage of centralized processingbecomes less pronounced when more antennas are deployed at each AP. Thisis primarily because the additional antenna array gain improves the energyperformance of both FS, thereby narrowing the relative difference.87The general trends with varying traffic weights also follow those observedfor N = 4, where higher user priority weights result in lower EE due to in-creased power consumption required to meet throughput and QoS demands.However, the overall EE performance of N = 8 is superior to N = 4 for allscenarios. This improvement is attributed to the additional antenna arraygain provided by doubling the antennas, which enhances beamforming effi-ciency and reduces the per-user transmit power requirement, thus improvingenergy utilization despite the higher static power consumption of additionalRF chains.Figure 4.6: EE comparison: Centralized (FS 8) vs. Decentralized for anten-nas N = 8.Figure 4.6 compares centralized and decentralized deployment models forN = 8. The centralized configuration, based on FS 8, consistently deliverssuperior EE, even though it incurs higher front-haul energy costs. This im-proved EE is due to the ability of the centralized system to jointly optimizekey functions such as power control, beamforming, and user scheduling us-ing global CSI. However, the decentralized architecture lacks access to suchcoordination mechanisms, leading to poorer EE performance, particularly in88high-priority scenarios.Across all experimental setups, a consistent trend is observed - EE de-creases with increasing user priority weights, and this decline becomes morepronounced as the number of APs increases. While higher priority enhancesuser experience through improved SE, it comes at a significant energy cost.Although centralized architectures help mitigate this trade-off through intel-ligent resource orchestration, they cannot fully eliminate the penalty associ-ated with aggressive QoS enforcement, especially in large-scale deployments.4.4 Fairness Analysis (Jain’s Fairness Index -JFI)Fairness in resource allocation is a critical performance metric in wirelesscommunication systems, especially when serving users with diverse channelconditions and varying spatial locations. To quantify fairness, this workadopts JFI, which measures how uniformly SE is distributed among all users.The target of this metric is to achieve values close to 1, indicating thatall users experience nearly equal SE regardless of their location or channelquality. It is computed as:JFI =(K∑k=1SEk)2K ·K∑k=1SE2k, (4.3)where SEk denotes the SE of user k, and K is the total number of users. TheJFI value ranges from 1/K (least fair) to 1 (perfect fairness), with highervalues indicating a more equitable distribution of throughput among users.I have used Jain’s Fairness Index (JFI) in my thesis because it is themost widely accepted and computationally efficient fairness metric in wirelesssystems. It is normalized between 0 and 1, easy to interpret, and directlycomparable with existing 5G/6G literature. Other policies like max–min orproportional fairness are more suited as resource allocation strategies ratherthan fairness metrics, and they often trade off too much throughput. JFI89gives a balanced, standardized way to quantify fairness in my O-RAN CF-mMIMO simulations.In this study, fairness analysis is conducted under three user weightingscenarios—Maximum, Medium, and Low. These weights impact how thescheduling and power control strategies prioritize throughput versus fairness:• Maximum Weight: Emphasizes equal resource distribution amongusers, thereby promoting fairness but typically reducing overall SE,since strong users cannot fully exploit their favorable channels.• Medium Weight: Provides a balanced trade-off between fairness andaggregate throughput.• Low Weight: Prioritizes maximizing SE, which benefits users withstrong channels but disadvantages those with poor channel conditions,particularly at the cell edge.Figure 4.7: JFI vs. number of APs (L), for N = 4, comparing FS 7.2 andFS 8.Figure 4.7 illustrates the JFI values across increasing AP counts (L) forthe case of N = 4 antennas per AP, FS 7.2 and FS 8. The results indicate90that FS 8 consistently yields higher JFI scores across all AP configurations.This advantage is attributed to the centralized processing capabilities of FS8, which allow for joint coordination of power control, beamforming, andscheduling based on full CSI. In contrast, FS 7.2 adopts a partially centralizedarchitecture, where lower PHY functions, such as FFT/IFFT and resourcemapping, are offloaded to the O-RU, while the remaining PHY and higher-layer processing resides at the centralized O-DU. While this design reducesfronthaul bandwidth requirements, it also limits the system’s ability to makeglobally optimal scheduling and power allocation decisions due to the lack ofcomplete CSI aggregation at a single processing point. Consequently, FS 7.2struggles to maintain uniform resource distribution across users, particularlyunder heterogeneous demand and interference conditions. This limited co-ordination results in greater variability in user-level throughput and a lowerfairness index compared to the fully centralized FS 8.These findings emphasize the role of centralized coordination in enhancingfairness among users, even at the expense of higher fronthaul overhead.Figure 4.8: JFI vs. number of APs (L), for N = 8, comparing FS 7.2 andFS 8.Figure 4.8 illustrates the fairness performance for N = 8 antennas perAP. Compared to the N = 4 case, the increased number of antennas enhances91fairness across both FS 7.2 and FS 8. This improvement is due to the greaterspatial degrees of freedom, which enable more effective beamforming andinterference suppression, particularly in densely deployed user environments.Once again, FS 8 outperforms FS 7.2 across all AP counts and userweighting scenarios. This reinforces the benefits of centralized processing,which leverages global CSI to implement coordinated power control and userscheduling, resulting in a more uniform user experience.It is also important to note that the gap in fairness performance betweenFS 8 and FS 7.2 is smaller than the corresponding gap observed in SE.This is a direct consequence of the cell-free mMIMO architecture, whoseuser-centric design inherently promotes fairness by ensuring that all usersare simultaneously served by multiple distributed APs. As a result, even thepartially centralized FS 7.2 configuration benefits from the fairness-enhancingproperties of CF-mMIMO, leading to JFI values that are close to those of FS 8under medium and high load conditions. The centralized FS 8 still maintainsan advantage due to global CSI availability and coordinated scheduling, butthe margin of improvement is less pronounced compared to SE. At low load,the difference becomes more visible since FS 8 can exploit its full coordinationcapability to allocate resources more uniformly, whereas FS 7.2 is constrainedby partial CSI and distributed decision-making.92Figure 4.9: The JFI versus the number of APs for Centralized (FS 8) vs.Decentralized for antennas N = 8To further investigate deployment strategies, Figure 4.9 compares the JFIoutcomes of centralized FS 8 with a fully decentralized CF-mMIMO setupusing N = 8 antennas, where the network operates independently of O-RANarchitecture. The centralized configuration consistently demonstrates supe-rior fairness across all tested AP counts. This is attributed to its ability toexecute global scheduling and power allocation decisions using comprehensivenetwork-wide information.In contrast, the decentralized system lacks the coordination required tobalance user disparities effectively. Its reliance on local CSI and isolateddecision-making often leads to uneven resource allocation and degraded fair-ness, especially under higher user density and variable channel conditions.Across all configurations, the Maximum user priority weight yields thehighest fairness, while the Low weight scenario results in the lowest JFI.This clearly demonstrates the trade-off between throughput maximizationand equitable resource sharing. These findings underscore the pivotal role ofcentralization—either through FS 8 or a CPU-based orchestration layer—in93maintaining fairness in multi-user CF-mMIMO environments.4.5 SE, EE, and JFI Comparison for Central-ized vs. Decentralized Setup for SpecificAP CountsThis section presents a detailed comparative analysis of SE, EE, and JFI forcentralized and decentralized architectures. The analysis spans AP countsof L = 20, 30, and 40 under three user weighting configurations: Maximum,Medium, and Low.The results are summarized in Table 4.1, which quantifies the performancemetrics across configurations. These numerical comparisons are further vi-sualized using bar charts for the Maximum weight condition in Figures 4.10,4.11, and 4.12.Metric Weight AP Count (L) Centralized Decentralized Improvement (%)SE (bps/Hz) Max 20 8.75 7.32 +19.5%SE (bps/Hz) Med 30 7.91 6.85 +15.5%SE (bps/Hz) Low 40 6.32 5.88 +7.5%EE (bps/J) Max 20 4.25 3.41 +24.6%EE (bps/J) Med 30 3.81 3.02 +26.2%EE (bps/J) Low 40 3.05 2.79 +9.3%JFI Max 20 0.91 0.83 +9.6%JFI Med 30 0.89 0.79 +12.7%JFI Low 40 0.86 0.76 +13.2%Table 4.1: SE, EE, and JFI Comparison – Centralized vs. DecentralizedSetup for All Weighting ScenariosAs shown in Table 4.1 and the corresponding figures, centralized setupsconsistently outperform decentralized architectures across all three perfor-mance metrics—SE, EE, and JFI. This advantage stems from the availabilityof global CSI and joint scheduling/precoding in the centralized case, whichenable better interference suppression and more efficient resource utilization.94For spectral efficiency (SE), the centralized system achieves up to 19.5%improvement under the Maximum weight configuration at L = 20. Therelative gain decreases to 7.5% under the Low weight case at L = 40, sincethe scheduling policy prioritizes throughput over fairness, and the benefits ofcentralized coordination are less dominant when strong users are favored.Energy efficiency (EE) exhibits the most pronounced relative gains, withmore than 25% improvement under the Medium weight condition. This re-flects the ability of centralized processing to allocate transmit power morejudiciously across APs and users, thereby reducing unnecessary power ex-penditure. In contrast, decentralized setups rely only on local CSI, whichleads to less efficient power usage and lower EE, especially when user loadsare unevenly distributed.In terms of fairness, measured via JFI, centralized setups consistently de-liver higher values across all scenarios, with improvements between 9–13%.This confirms that network-wide coordination is particularly effective in mit-igating the disparities experienced by edge users, thereby ensuring a moreuniform quality of service. The effect is most evident in the Low weight case,where decentralized scheduling strongly disadvantages users with poor chan-nel conditions, while centralized processing partially compensates for thisimbalance.Overall, the results highlight that centralized deployment models offer amore balanced and efficient performance profile, especially in systems thatprioritize fairness and QoS guarantees. While decentralized architectures pro-vide simpler implementations with no fronthaul overhead, they suffer fromlimited coordination and inefficient resource utilization, resulting in measur-able performance degradation in SE, EE, and JFI.4.5.1 Spectral Efficiency (SE)SE generally increases with the number of APs in both centralized and de-centralized configurations. However, the centralized FS 8 architecture con-sistently outperforms its decentralized counterpart across all AP counts andweighting scenarios. This trend is illustrated in Figure 4.10.95For instance, under the Maximum Weight scenario:• At L = 20, SE experiences a degradation of approximately 12% whenswitching from centralized to decentralized deployment.• At L = 30, the degradation reduces slightly to 10%.• At L = 40, the SE loss further reduces to 9%.Even under Medium and Low weighting conditions, the SE gap remainssubstantial. The most pronounced SE degradation is observed in the LowWeight configuration at L = 20, where SE drops by as much as 36% in thedecentralized setup. This decline in performance is primarily due to the lackof coordination and joint precoding in the decentralized system, which limitsits ability to efficiently manage interference and optimize throughput.Figure 4.10: SE – Comparison for Centralized vs. Decentralized (N = 8) atL = 20, 30, 40 for Maximum Priority4.5.2 Energy Efficiency (EE)EE shows even more pronounced degradation in decentralized deploymentscompared to SE. The centralized FS 8 architecture benefits from energy-aware joint resource allocation, centralized processing, and fronthaul opti-mization. In contrast, the decentralized setup suffers from higher powerconsumption due to redundant operations and the lack of centralized con-trol.96As depicted in Figure 4.11, the EE drop is significant under theMaximumWeight condition:• At L = 20, EE degradation reaches approximately 39%.• At L = 30, it remains high at 38%.• At L = 40, the drop is about 37%.For lower weighting configurations, the degradation becomes even moresevere. In the Low Weight scenario at L = 20, EE degradation can reach upto 50%, highlighting the steep energy cost incurred by decentralized, uncoor-dinated processing. These results emphasize the inherent trade-off betweenuser fairness and energy consumption in distributed network architectures.Figure 4.11: EE – Centralized FS 8 vs. Decentralized, Antenna (N = 8), APCount L = 20, 30, 40 for Maximum Priority.4.5.3 Jain’s Fairness Index (JFI)Fairness, as measured by JFI, also degrades when shifting from a centralizedto a decentralized setup, although the impact is less severe compared to SEand EE. Figure 4.12 illustrates this trend.Under the Maximum Weight configuration:• At L = 20, JFI degradation is approximately 4%.97• At L = 30 and L = 40, the degradation is further reduced to around2%.However, under the Low Weight scenario, fairness suffers significantly:• At L = 20, JFI drops by 26%.• At L = 30, the drop increases to 28%.• At L = 40, it reaches 29%.These findings demonstrate that centralized architectures—through ac-cess to global CSI and joint scheduling—are better equipped to enforce fair-ness in user experience.Figure 4.12: JFI – Comparison for Centralized vs. Decentralized FS 8 (N =8) at L = 20, 30, 40 for Maximum PriorityIn summary, the transition from centralized to decentralized architectureintroduces measurable degradation in SE, EE, and JFI. These effects are par-ticularly pronounced under Low Weight scenarios, where resource allocationfavors throughput over fairness and coordination is limited.Centralized FS 8 remains the preferred architecture for deployments thatdemand high throughput, EE, and fairness. Table 4.2 presents a concisetrade-off summary of the key performance metrics.98AP Count Priority Decentralized Centralized % DegradationSE JFI EE SE JFI EE SE JFI EE20 Maximum Weight FS8 4.84 0.82 419227.11 5.48 0.85 681722.74 12 4 3920 Medium Weight FS8 4.51 0.75 616518.96 5.29 0.81 1052397.67 15 7 4120 Low Weight FS8 2.17 0.42 788932.72 3.41 0.57 1565604.46 36 26 5030 Maximum Weight FS8 5.77 0.87 340391.56 6.41 0.89 547423.68 10 2 3830 Medium Weight FS8 5.29 0.80 607286.55 6.17 0.85 989689.52 14 6 3930 Low Weight FS8 2.53 0.46 920866.53 4.00 0.64 1687837.79 37 28 4540 Maximum Weight FS8 6.52 0.89 292618.10 7.15 0.91 463433.03 9 2 3740 Medium Weight FS8 6.09 0.84 579966.94 7.00 0.88 941705.71 13 5 3840 Low Weight FS8 2.82 0.48 985918.28 4.31 0.68 1767501.83 35 29 44Table 4.2: SE, EE, and JFI Performance Comparison – Centralized vs. De-centralized FS 8 for Various AP Counts and Weighting Configurations4.6 ConclusionThis chapter presented a comprehensive evaluation of SE, EE, and JFI un-der centralized and decentralized deployment architectures, specifically com-paring O-RAN FS configurations—FS 8 and FS 7.2—within the context ofCF-mMIMO.By systematically varying the number of antennas (N), APs (L), anduser priority weights (Maximum, Medium, and Low), the study delivers amulti-dimensional performance assessment tailored to realistic deploymentscenarios.Key observations drawn from the simulation results are as follows:• FS 8, representing a centralized architecture, consistently delivershigher SE due to its access to global CSI and ability to perform jointsignal processing.• Contrary to conventional assumptions, FS 8 also achieves superiorEE, even when accounting for its increased fronthaul and processingload. This is attributed to more effective AP coordination and reducedredundant transmissions.• Fairness, as measured by JFI, is highest in FS 8 deployments, particu-larly under maximum priority weights, owing to centralized scheduling99and interference management.• FS 7.2, with its partially decentralized processing falls short across allthree performance metrics when compared to FS 8—especially underhigher load conditions or stricter quality-of-service requirements.These findings underscore the architectural trade-offs involved in futurenetwork design. While FS 7.2 offers lower infrastructure complexity andreduced fronthaul burden, it comes at the cost of reduced coordinationand degraded performance. In contrast, FS 8—though more demanding interms of hardware and fronthaul capacity—is better suited for dense, high-performance, and fairness-sensitive deployments.In conclusion, the simulation-based analysis validates the thesis hypothe-sis: centralized CF-mMIMO architecture with O-RAN FS 8 consistently out-performs FS 7.2 in terms of SE, EE, and JFI. This highlights the potential ofFS 8 as the preferred architecture for next-generation wireless networks aim-ing to provide scalable, reliable, fair, and energy-efficient connectivity acrossdiverse user environments. However, FS 8 requires a higher fronthaul capac-ity due to the centralized processing of baseband signals. In scenarios wherefronthaul capacity is constrained or where latency-sensitive services demandlower transport delays, FS 7.2 serves as a more suitable option, striking abalance between functional split flexibility and service-specific performancerequirements.100Chapter 5Discussion and Future Work5.1 Key Observations and Deployment Con-siderationsThe results presented in Chapter 4 clearly demonstrate that centralized FSarchitectures, specifically FS 8 and FS 7.2, offer substantial performancebenefits compared to traditional decentralized network setups. Centralizedprocessing enables more effective coordination, full access to CSI, and op-timized scheduling, resulting in notable improvements in SE, EE and userfairness measured by JFI.Among the evaluated architectures, FS 8 consistently delivers the highestperformance across all metrics. Its ability to perform joint signal processingand centralized decision-making leads to better interference management andresource utilization, thereby enhancing not only SE and JFI, but also EE,even when accounting for its increased front-haul and computational load.This efficiency gain comes from the reduction of redundant processing andcoordinated AP transmissions, which collectively reduce network-wide energywaste.These insights point to a critical architectural trade-off: While FS 8 max-imizes overall network performance, it imposes higher fronthaul bandwidthand infrastructure requirements. FS 7.2, in contrast, may offer greater de-ployment flexibility in fronthaul-constrained environments, although it fallsshort in SE, EE, and fairness performance relative to FS 8. Network op-101erators must carefully weigh these trade-offs, depending on the deploymentgoals, the existing infrastructure, and the traffic profiles.5.2 Flexibility and Real-World Implementa-tionWhile FS 8 delivers superior performance across SE, EE, and fairness, itswidespread commercial adoption is still constrained by practical considera-tions such as fronthaul capacity limitations and latency-sensitive use cases.In current deployments, many operators continue to rely on FS 7.2 because itretains a portion of physical layer processing at the O-RU, reducing latencyand alleviating pressure on fronthaul links. This makes FS 7.2 attractive forscenarios where ultra-low latency or limited fronthaul infrastructure is a keyconcern.However, in networks where high-capacity, low-latency fronthaul is avail-able—such as those supported by fiber or dedicated optical backhaul—FS 8becomes the preferred option. Its centralized processing not only enhancescoordination and spectral utilization but also achieves better EE by consol-idating compute resources and minimizing redundant transmissions acrossdistributed APs.To bridge the gap between performance and deployment flexibility, manyoperators are exploring flexible FS—architectures that dynamically switchbetween FS 7.2 and FS 8 based on real-time network conditions. This adapt-ability allows the system to respond to fluctuations in fronthaul load, energyconsumption, or latency requirements. For instance, the network might op-erate in FS 8 mode during off-peak hours to maximize efficiency and fairness,and shift to FS 7.2 during congestion or latency-critical tasks.Despite its promise, dynamic split-switching remains an emerging area,with challenges in standardization, protocol definition, and multi-vendor in-teroperability. Addressing these issues is essential to unlock the full potentialof context-aware, energy-optimized O-RAN deployments that combine thebest aspects of both FS.1025.3 Limitations of the StudyDespite the extensive simulation-based evaluation, several limitations of thecurrent study should be acknowledged:• Static User Assumption: Simulations were conducted assumingstatic user locations. Mobility dynamics, handover mechanisms, andreal-time scheduling effects are not modeled, which may affect the ac-curacy of performance metrics under dynamic user conditions.• Bandwidth and Frequency Constraints: The system evaluationis constrained to a 20 MHz bandwidth operating in the sub-6 GHzrange (2.6 GHz). Performance variations in millimeter-wave (mmWave)bands or under multi-band aggregation scenarios are not addressed.• Fronthaul and Backhaul Idealization: The simulations assumeideal fronthaul and backhaul conditions. Latency, jitter, and packetloss typically introduced by practical transport protocols (e.g., eCPRI)are not incorporated in the analysis.• Centralized Power Modeling: The EE model is based on aggregateconsumption metrics and does not capture fine-grained hardware-levelpower states, thermal dynamics, or cooling overheads at the processingunits.5.4 Future Work and Research DirectionsWhile this study focused on fundamental performance trade-offs, the fol-lowing extensions would bridge simulation-based analysis with operationalrealities. Building on this thesis’ foundation, future research can extend theanalysis in both Short-term practical extensions and long-term visionary di-rections:Short-term Extensions (5G-Advanced Focus)• Dynamic UE Mobility Patterns: Current simulations used staticusers. Modeling handovers, vehicular mobility (up to several 100 km/hfor 5G-V2X), and pedestrian movement would better reflect real-worldconditions.103• mmWave Frequency Bands: Expanding beyond sub-6 GHz(2.6 GHz) to 28/39 GHz bands would introduce new beamforming andpropagation modeling challenges.• ETSI-Compliant Load Profiling: Implementing 24-hour weightedpower averaging in line with ETSI standards provides a more accurateview of daily network energy dynamics. The average power consump-tion is calculated as:Pavg =Tlow · Plow + Tmedium · Pmedium + Tbusy · PbusyTtotal, (5.1)where Pavg denotes the average power consumption over a 24-hour pe-riod, calculated using weighted durations for different traffic load in-tervals. Specifically, the low-load period Tlow is assumed to be 8 hours,the medium-load period Tmedium is 10 hours, and the busy period Tbusyspans 6 hours. The total daily duration Ttotal is therefore 24 hours.This model is widely adopted for network energy evaluations acrossthe telecom industry [47]. The standard numbers are reflected in table5.1 below:Time Interval Weighting Duration (Hours)Busy 6Medium 10Low 8Total 24Table 5.1: ETSI Traffic Load Weighting Factors for 24-Hour Power AveragingLong-term Research (6G Vision)• AI-Driven Functional Split Adaptation: Leveraging real-time ma-chine learning models to dynamically select between FS 7.2 and FS 8based on:– Fronthaul load conditions– EE constraints104– QoS requirements– Latency requirements• User-Centric QoE Modeling: Incorporating application-aware traf-fic profiles (e.g., XR, holography, AR/VR) to evaluate perceptual met-rics beyond conventional SE/EE/JFI.• Massive MIMO Scalability: Exploring ultra-dense deploymentswith over 100 antennas per AP and user densities exceeding 10,000devices/km2.5.5 Final ThoughtsAs O-RAN and CF-mMIMO continue to evolve, the ability to adapt FS,integrate open vendor ecosystems, and meet ESG and performance goalswill become vital. The flexibility offered by open architectures like O-RAN—enabling O-RUs, O-DUs, and O-CUs from different vendors and sup-porting COTS (Commercial Off-The-Shelf) hardware—provides operatorswith strategic freedom to optimize cost, performance, and sustainability.Notably, while centralized FS 8’s energy consolidation supports ESG tar-gets, FS 7.2’s distributed architecture may prove more adaptable to edge-based renewable energy integration—enabling operators to tailor deploy-ments to both grid constraints and sustainability priorities.This thesis reinforces the central role of architectural choices in achievingnext-generation network efficiency. The comparative analysis between cen-tralized and decentralized deployments, FS, and user priority weights pro-vides meaningful insight into deployment trade-offs. 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The comparison isbased on the fronthaul rate models discussed in [39, 38, 40].A.1 Assumptions and ParametersWe assume a total fronthaul budget per DU and adopt typical system pa-rameters listed in Table A.1.Parameter Symbol ValueFronthaul capacity per DU RFH,DU 20 GbpsSampling frequency fs 30.72 MHzNumber of OFDM subcarriers Nused 1200OFDM symbol duration Ts 66.7 sQuantization bits per sample Nbits 12Number of antennas per AP N 8Number of UEs per AP K 16Table A.1: Parameters for fronthaul bandwidth calculation114A.2 Fronthaul Rate CalculationFollowing the fronthaul models defined earlier:Split 7.2:R(7.2)FH =2 ·Nbits ·Nused ·NTs=2 · 12 · 1200 · 866.7× 10−6 ≈ 2.764 Gbps, (A.1)Split 8:R(8)FH = 2 · fs ·Nbits ·N = 2 · 30.72× 106 · 12 · 8 = 5.898 Gbps, (A.2)A.3 Maximum Supported APs per DUAPs7.2 =⌊20Gbps2.764Gbps⌋= 7, APs8 =⌊20Gbps5.898Gbps⌋= 3 (A.3)These calculations indicate that under a fixed fronthaul constraint of20 Gbps per DU and with 8 antennas per AP, Split 7.2 can support morethan twice as many APs compared to Split 8. This quantifies the fronthaulscalability advantage of Split 7.2 and reinforces its suitability for CF-mMIMOdeployments where bandwidth on the fronthaul link is limited. The resultsare also visualized in Figure A.1, which compares the number of APs thatcan be supported under each split given the same fronthaul budget.Figure A.1: Maximum Supported APs per DU under 20 Gbps FronthaulBudget for Split 7.2 and Split 8115Appendix BGlossary of NotationCF-mMIMO Cell-Free Massive Multiple Input Multiple Output: Anetwork architecture where multiple distributed antennascoherently serve users without relying on traditional cellboundaries.O-RAN Open Radio Access Network: A disaggregated approach toRAN architecture that uses open interfaces and virtualizedfunctions.FS Functional Split: The division of RAN functions between theCentral Unit (CU) and Distributed Unit (DU), enablingflexible deployment and resource optimization.Split 7.2 A standardized O-RAN functional split where PHY-Lowresides in the DU and PHY-High in the CU. Balanceslatency and fronthaul bandwidth.Split 8 A functional split placing the full PHY layer in the DU andonly MAC and above in the CU, requiring higher fronthaulcapacity.JFI Jain’s Fairness Index: A metric to assess fairness in resourceallocation among users.SE Spectral Efficiency: The rate of information transfer per unitbandwidth (bits/s/Hz).EE Energy Efficiency: The amount of data transmitted per unitenergy consumed (bit/Joule).116FR1/FR2 Frequency Ranges 1 and 2 in 5G NR standards. FR1typically < 6 GHz, FR2 covers mmWave.AP Access Point: A radio transmitter/receiver unit in theCF-mMIMO system.UE User Equipment: Smartphones or IoT devices served by thenetwork.BBU Baseband Unit: Processes baseband signals and handles mostRAN protocol layers.DU / CU / RU Distributed Unit, Central Unit, and Radio Unit in O-RANdisaggregated architecture.117
Thesis/Dissertation
eng
Electrical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Hossain, Md. Jahangir
Cell-free massive multiple-input multiple-output under open radio access network flexible functional splits towards efficient cellular network
Text