Applied Science, Faculty of
Electrical and Computer Engineering, Department of
DSpace
UBCV
Beckett, Marley
Master of Applied Science - MASc
University of British Columbia
The proliferation of Internet of Things (IoT) devices has transformed the technological landscape by embedding smart functionalities into everyday objects. Despite these advancements, most IoT devices continue to rely on traditional batteries, which present challenges such as limited lifespan, frequent replacements, and environmental concerns due to electronic waste. Ambient IoT, a technology currently undergoing standardization, seeks to address these issues by enabling devices to operate sustainably using harvested environmental energy. However, standardization efforts have not yet fully addressed energy harvesting, leaving its practical deployment uncertain. Focusing on radio frequency (RF) energy harvesting, this work examines holistic models to evaluate the performance of energy harvesting systems within the context of Ambient IoT. By examining ongoing standardization efforts and identifying the unique design requirements of Ambient IoT devices, we illustrate how Ambient IoT differs from existing technologies. Key performance metrics, specifically energy harvesting coverage and charging probability, are introduced to assess the effectiveness of various RF energy sources. This thesis further explores the feasibility of powering Ambient IoT devices through ambient RF sources by integrating real-world measurements and generating pseudo-ambient data that represents the available power across various lo- cations. To account for the inherent variability in RF signals, an autoregressive process is employed to model the correlations between data points. The numerical results demonstrate that while ambient RF energy harvesting is viable in outdoor settings, indoor environments pose significant challenges due to low mean power densities. In addition to ambient RF sources, we examine dedicated RF energy sources to improve the reliability and coverage of power delivery for Ambient IoT devices. By strategically deploying dedicated sources using point processes related to real-world communication infrastructure and integrating multi-antenna systems, numerical results indicate significant enhancements in energy harvesting, especially in power-limited indoor environments.
Harnessing RF Energy for Sustainable Power in AmbientIoT DevicesbyMarley BeckettB.ASc., Queen’s University, 2021A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMaster of Applied ScienceinTHE FACULTY OF GRADUATE AND POSTDOCTORALSTUDIES(Electrical and Computer Engineering)The University of British Columbia(Vancouver)October 2024© Marley Beckett, 2024The following individuals certify that they have read, and recommend to the Fac-ulty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled:Harnessing RF Energy for Sustainable Power in Ambient IoT Devicessubmitted by Marley Beckett in partial fulfillment of the requirements for the de-gree of Master of Applied Science in Electrical and Computer Engineering.Examining Committee:Lutz Lampe, Professor, Electrical and Computer Engineering, UBCSupervisorSudip Shekhar, Professor, Electrical and Computer Engineering, UBCSupervisory Committee MemberLele Wang, Assistant Professor, Electrical and Computer Engineering, UBCSupervisory Committee MemberiiAbstractThe proliferation of Internet of Things (IoT) devices has transformed the techno-logical landscape by embedding smart functionalities into everyday objects. De-spite these advancements, most IoT devices continue to rely on traditional batteries,which present challenges such as limited lifespan, frequent replacements, and envi-ronmental concerns due to electronic waste. Ambient IoT, a technology currentlyundergoing standardization, seeks to address these issues by enabling devices tooperate sustainably using harvested environmental energy. However, standardiza-tion efforts have not yet fully addressed energy harvesting, leaving its practicaldeployment uncertain.Focusing on radio frequency (RF) energy harvesting, this work examines holis-tic models to evaluate the performance of energy harvesting systems within thecontext of Ambient IoT. By examining ongoing standardization efforts and iden-tifying the unique design requirements of Ambient IoT devices, we illustrate howAmbient IoT differs from existing technologies. Key performance metrics, specifi-cally energy harvesting coverage and charging probability, are introduced to assessthe effectiveness of various RF energy sources.This thesis further explores the feasibility of powering Ambient IoT devicesthrough ambient RF sources by integrating real-world measurements and gener-ating pseudo-ambient data that represents the available power across various lo-cations. To account for the inherent variability in RF signals, an autoregressiveprocess is employed to model the correlations between data points. The numericalresults demonstrate that while ambient RF energy harvesting is viable in outdoorsettings, indoor environments pose significant challenges due to low mean powerdensities.iiiIn addition to ambient RF sources, we examine dedicated RF energy sourcesto improve the reliability and coverage of power delivery for Ambient IoT devices.By strategically deploying dedicated sources using point processes related to real-world communication infrastructure and integrating multi-antenna systems, numer-ical results indicate significant enhancements in energy harvesting, especially inpower-limited indoor environments.ivLay SummaryThe Internet of Things (IoT) represents an expanding network of devices that con-nect and share data, fundamentally changing our interaction with technology. Tra-ditional IoT devices rely on batteries, which can limit their use in difficult envi-ronments and contribute to electronic waste. Ambient IoT envisions a new classof devices that operate without batteries by drawing energy from their surround-ings. However, there is no standardized method yet for how these devices willbe powered. This thesis investigates the use of radio frequency energy harvest-ing to power Ambient IoT devices. We examine both ambient and dedicated radiofrequency sources to understand the limitations and capabilities of Ambient IoT invarious environments. By analyzing existing models for energy harvesting systemsand adapting them to the specific requirements of Ambient IoT while establishingperformance metrics, we aim to ensure the reliable operation of future AmbientIoT technologies.vPrefaceThis thesis presents original work by the author, Marley Beckett. Portions of Chap-ter 2 and 5 have been submitted for publication as: M. Beckett, L. Lampe, G. Vos,“Empowering Ambient IoT: Strategies for Reliable Energy Harvesting”. I origi-nated the research concept, conducted the analysis and simulations, and wrote themanuscript. Gus Vos, an industry collaborator from Semtech Corp., contributedby helping shape the research problems and aligning the work with current andemerging industry requirements.All research was conducted under the supervision of Professor Lutz Lampein the Department of Electrical and Computer Engineering at the University ofBritish Columbia, who assisted in developing the research directions and revisingmanuscripts.viTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiiList of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . xviAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xix1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Motivation and Background . . . . . . . . . . . . . . . . . . . . 11.1.1 Evolution of IoT . . . . . . . . . . . . . . . . . . . . . . 21.1.2 Energy Harvesting . . . . . . . . . . . . . . . . . . . . . 31.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.3 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Wireless RF Energy Harvesting . . . . . . . . . . . . . . . . . . . . 82.1 RF Wave Propagation . . . . . . . . . . . . . . . . . . . . . . . . 92.1.1 Antenna Operating Regions . . . . . . . . . . . . . . . . 9vii2.1.2 Propagation Channel . . . . . . . . . . . . . . . . . . . . 102.1.3 Shadow Fading . . . . . . . . . . . . . . . . . . . . . . . 122.2 RF Energy Harvesting Receiver . . . . . . . . . . . . . . . . . . 132.2.1 Rectenna . . . . . . . . . . . . . . . . . . . . . . . . . . 142.2.2 Power Management Unit . . . . . . . . . . . . . . . . . . 162.3 System Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . 182.3.1 Harvested Power . . . . . . . . . . . . . . . . . . . . . . 182.3.2 Stored Energy . . . . . . . . . . . . . . . . . . . . . . . . 243 Ambient IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.1 Standardization of Ambient IoT . . . . . . . . . . . . . . . . . . 283.2 Existing IoT Solutions . . . . . . . . . . . . . . . . . . . . . . . 303.2.1 RFID . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.2.2 Unlicensed LPWAN . . . . . . . . . . . . . . . . . . . . 323.2.3 Licensed LPWAN . . . . . . . . . . . . . . . . . . . . . 333.3 Related Technologies . . . . . . . . . . . . . . . . . . . . . . . . 333.3.1 Backscattering . . . . . . . . . . . . . . . . . . . . . . . 343.3.2 SWIPT . . . . . . . . . . . . . . . . . . . . . . . . . . . 343.3.3 Cognitive Radio . . . . . . . . . . . . . . . . . . . . . . 353.4 Ambient IoT Overview . . . . . . . . . . . . . . . . . . . . . . . 363.4.1 Design Targets and Requirements . . . . . . . . . . . . . 363.4.2 Applications . . . . . . . . . . . . . . . . . . . . . . . . 373.5 Challenges in Device Powering . . . . . . . . . . . . . . . . . . . 374 Harvesting from Ambient RF Sources . . . . . . . . . . . . . . . . . 404.1 Ambient RF Energy Sources . . . . . . . . . . . . . . . . . . . . 414.1.1 Capturing Energy . . . . . . . . . . . . . . . . . . . . . . 424.1.2 Measurement Campaigns . . . . . . . . . . . . . . . . . . 434.2 Data Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . 454.3 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . 484.3.1 Harvesting Coverage . . . . . . . . . . . . . . . . . . . . 484.3.2 Charging Probability . . . . . . . . . . . . . . . . . . . . 494.4 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . 50viii4.4.1 Device Model . . . . . . . . . . . . . . . . . . . . . . . . 504.4.2 Energy Harvesting Model . . . . . . . . . . . . . . . . . 514.4.3 Coverage Analysis . . . . . . . . . . . . . . . . . . . . . 514.4.4 Charging Probability . . . . . . . . . . . . . . . . . . . . 525 Harvesting from Dedicated RF Sources . . . . . . . . . . . . . . . . 565.1 Deployment of PBs . . . . . . . . . . . . . . . . . . . . . . . . . 575.1.1 Stochastic Geometry . . . . . . . . . . . . . . . . . . . . 575.1.2 Practical Implementations . . . . . . . . . . . . . . . . . 595.2 Multi-antenna PBs . . . . . . . . . . . . . . . . . . . . . . . . . 605.2.1 Uniform Linear Array . . . . . . . . . . . . . . . . . . . 615.2.2 CSI-free Energy Beamforming . . . . . . . . . . . . . . . 615.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645.3.1 Signal Model . . . . . . . . . . . . . . . . . . . . . . . . 645.3.2 Channel Model . . . . . . . . . . . . . . . . . . . . . . . 665.3.3 CSI-Free Multi-antenna Schemes . . . . . . . . . . . . . 675.4 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . 705.4.1 Deployment Scenarios . . . . . . . . . . . . . . . . . . . 705.4.2 Coverage Analysis . . . . . . . . . . . . . . . . . . . . . 715.4.3 Charging Probability . . . . . . . . . . . . . . . . . . . . 736 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . 766.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 766.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79A 3GPP Network Infrastructure Parameters . . . . . . . . . . . . . . 90B 3GPP Channel Design Models . . . . . . . . . . . . . . . . . . . . . 91ixList of TablesTable 1.1 Comparison of power density, predictability, and controllabilityof various ambient energy harvesting sources [1, 2]. . . . . . . 5Table 2.1 Comparison of power harvesting models used in radio frequency(RF) energy harvesting systems literature. The models are char-acterized for their ability to model sensitivity, a non-linear powerconversion efficiency (PCE), and saturation. . . . . . . . . . . 22Table 3.1 Current state of Ambient IoT device and network design tar-gets for 3rd Generation Partnership Project (3GPP) and Instituteof Electrical and Electronics Engineers (IEEE) standardizationefforts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37Table 3.2 3GPP representative use cases for Ambient IoT, along with ap-plication scenarios for each, based on initial use cases from TR28.840 [3]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38Table 4.1 Frequency bands of interest for ambient RF energy harvesting,highlighting the main transmitting sources for each band. Thesources are categorized by type (static or dynamic) to illustratepotential contributors to ambient RF energy availability. . . . . 41Table 4.2 Ambient RF measurements for sub-1 GHz communication bandsacross three locations: Montreal, Canada; Isparta, Turkey; andParma, Italy. The table presents the distribution of measuredambient RF energy in these regions. . . . . . . . . . . . . . . . 44xTable 4.3 Updated ambient RF measurements from Table 4.2, featuringadjusted dBm means calculated using Equation 4.5 and replac-ing the previously reported linear means under the assumptionof a log-normal distribution. . . . . . . . . . . . . . . . . . . . 46Table 4.4 Simulation parameters for a general Ambient Internet of Things(IoT) device with RF energy harvesting capabilities. . . . . . . 50Table 4.5 Parameters for the sigmoid RF energy harvesting model in Ta-ble 2.1 and DC-DC PCE of the power management unit (PMU). 51Table 4.6 Probability of energy harvesting coverage for ambient RF powermeasurement values, adjusted for effective bandwidth, usingEquation 4.10. . . . . . . . . . . . . . . . . . . . . . . . . . . 52Table 4.7 Correlation coefficients considered for the numerical results ingenerating pseudo-ambient data for analyzing ambient RF en-ergy sources. . . . . . . . . . . . . . . . . . . . . . . . . . . . 53Table 4.8 Charging probability of an Ambient IoT device at the ambientRF measurement locations in Table 4.3. . . . . . . . . . . . . . 54Table 5.1 Stochastic geometry point processes and their relevance to cur-rent wireless networking infrastructure. . . . . . . . . . . . . . 60Table 5.2 Simulation parameters for deploying dedicated power beacons(PBs) to power Ambient IoT devices. Three scenarios are con-sidered. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71Table 5.3 Energy harvesting coverage resulting from deployments of PBs.Coverage is evaluated for four CSI-free transmission schemesacross various deployment densities of PBs. . . . . . . . . . . 72Table 5.4 Charging probabilities for Ambient IoT devices resulting fromdeployments of PBs. The charging performance is evaluated forfour CSI-free transmission schemes across various PB deploy-ment densities. . . . . . . . . . . . . . . . . . . . . . . . . . . 73Table A.1 Simulation assumptions for 3GPP fifth generation (5G) cellularbase station infrastructure [4, 5]. . . . . . . . . . . . . . . . . 90xiTable A.2 Simulation assumptions for 3GPP 5G cellular user equipmentinfrastructure [4, 5]. . . . . . . . . . . . . . . . . . . . . . . . 90Table B.1 Path loss and shadow fading channel design parameters for 3GPPInH model [4]. . . . . . . . . . . . . . . . . . . . . . . . . . . 91Table B.2 Path loss and shadow fading channel design parameters for 3GPPInF models [4]. . . . . . . . . . . . . . . . . . . . . . . . . . . 92xiiList of FiguresFigure 1.1 The design targets set out for Ambient IoT as envisioned by3GPP in the TSG SA working group 1 study item [3]. . . . . . 3Figure 2.1 Propagation of RF waves in free-space, highlighting the distri-bution of near-field and far-field regions. . . . . . . . . . . . . 10Figure 2.2 Path loss in decibels (dB) versus distance between a transmitterand receiver for free-space line-of-sight (LOS) and 3GPP LOS5G network models. . . . . . . . . . . . . . . . . . . . . . . 12Figure 2.3 General architecture of a harvest-store-use RF energy harvest-ing receiver designed for integration with an Ambient IoT de-vice, shown here as a general load. . . . . . . . . . . . . . . . 14Figure 2.4 Ragone plot comparing power density and energy density forcapacitors, supercapacitors, and batteries. Inspired by [6], thisplot illustrates that capacitors and supercapacitors exhibit highpower density but low energy density in contrast to batteries. . 17Figure 2.5 Received power (dBm) as a function of distance from varioustransmitting sources assuming GTx and GRx are both 0 dBi. . . 20Figure 2.6 Comparison of power conversion models for RF energy har-vesting with data from a real system in [7] with input power inthe milliwatt range. . . . . . . . . . . . . . . . . . . . . . . . 23xiiiFigure 2.7 Voltage plot illustrating the charging and discharging cycles ofan energy storage capacitor in an Ambient IoT device. The de-vice experiences multiple charging phases, followed by activeoperation periods where the capacitor discharges (e.g., duringON states). . . . . . . . . . . . . . . . . . . . . . . . . . . . 25Figure 3.1 Positioning of Ambient IoT compared to RFID and existingLPWAN technologies for IoT. Ambient IoT is envisioned toexist between these sets of technologies regarding power con-sumption during a transmission, range, and device cost [8, 9]. 31Figure 4.1 Ambient RF energy profile example in Boston, MA, showingthe incident RF power along with the general variation trendand mean power levels [10]. . . . . . . . . . . . . . . . . . . 42Figure 4.2 Generated 1,000 samples following an AR(1) process with cor-relation coefficient p. The data is based on the mean and stan-dard deviation of the measurements from the Canada urbanlocation in Table 4.3. . . . . . . . . . . . . . . . . . . . . . . 48Figure 4.3 Example of the storage capacitor voltage for Turkey’s semi-indoor location A under medium correlation conditions. Whilecharge begins to accumulate in the storage capacitor, the volt-age levels off around 0.3 V, ultimately resulting in very lowcharging probabilities for this scenario. . . . . . . . . . . . . 55Figure 5.1 Examples of point processes, each normalized to unit dimen-sions. All processes are simulated with an intensity of λ =0.0025. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59Figure 5.2 Structure of a 4 element ULA transmitter, with θ represent-ing the steering angle relative to the boresight shown in blue.The distance between subsequent antenna elements is d, whichwe assume to be equal to λ/2 for the case of half-wavelengthspacing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61xivFigure 5.3 Gain patterns for a 4-element half-wavelength spaced uniformlinear array (ULA) arranged along the y-axis in the azimuthplane. These plots displays a vertical cut of the 3D radiationpattern at zero elevation. . . . . . . . . . . . . . . . . . . . . 62Figure 5.4 Energy harvesting coverage for various CSI-free transmissionschemes considered in this work. The green and orange cov-erage areas illustrate the radiation patterns for different beamsteering angles and mechanical rotations in SEB, REB, andMR configurations. . . . . . . . . . . . . . . . . . . . . . . . 63xvList of Abbreviations3GPP 3rd Generation Partnership Project4G fourth generation5G fifth generation6G sixth generationAC alternating currentAMP ambient power-enabled devicesAP access pointsBLE Bluetooth Low EnergyBS base stationsCSD Criteria for Standards DevelopmentCSI channel state informationdB decibelsDC direct currentDTV digital TVEB energy beamformingEM electromagneticESR equivalent series resistanceGSM Global System for Mobile communicationIEEE Institute of Electrical and Electronics Engineersxvii.i.d. independent and identically distributedInF indoor factoryInH indoor hotspotIoT Internet of ThingsLOS line-of-sightLPWAN Low Power Wide Area NetworkLTE Long Term EvolutionLTE-M LTE Machine Type CommunicationsMHCP Mate´rn hard-core processMISO multiple-input single-outputMR mechanical rotationNB-IoT Narrowband IoTNLOS non-line-of-sightOA one-antennaPAR Project Authorization RequestPB power beaconPCE power conversion efficiencyPMU power management unitPPP Poisson point processRAN Radio Access NetworkRBW resolution bandwidthREB random energy beamformingRF radio frequencyRFID Radio Frequency IdentificationSA System AspectsSEB sequential energy beamformingxviiSIG Special Interest GroupSWIPT simultaneous wireless information and power transferTIG Technical Interest GroupTSG Technical Specification GroupUE user equipmentULA uniform linear arrayUMa urban macrocellUMi urban microcellWLAN Wireless Local Area NetworkWSN wireless sensor networkWPT wireless power transferxviiiAcknowledgmentsFirst and foremost, I would like to extend my deepest gratitude to Dr. Lutz Lampefor his invaluable guidance and support throughout my research. His keen attentionto detail, clear communication, and unwavering enthusiasm for this work wereinstrumental in driving the project forward. Without his mentorship, this thesiswould not have been possible.This research was supported by MITACS, the Natural Sciences and Engineer-ing Research Council of Canada (NSERC), and Semtech Corp. Sincere thanks goto Mr. Gustav Vos and Mr. Serkan Dost from Semtech for their guidance, expo-sure to the industry, and ongoing support. Ambient IoT, being a new and evolvingtechnology without formalized standards, posed unique challenges, especially asthe requirements of my work shifted to align with industry developments. Thecontributions of our industry partners were essential to navigating this evolvinglandscape.xixChapter 1Introduction1.1 Motivation and BackgroundIn recent years, the concept of a smart world has transitioned from a visionary ideato a tangible reality, driven by advancements in connectivity and digital technolo-gies. Central to this development is the Internet of Things (IoT), which extendsthe Internet into the physical world through devices that feature pervasive networkconnectivity. These IoT devices wirelessly communicate with their surroundings,enabling the seamless exchange of data. The global IoT market is estimated tosurpass 80 billion connected devices by 2030 [11].Despite its immense potential, the rapid expansion of IoT devices poses sub-stantial challenges, particularly regarding how these devices are powered. MostIoT devices rely on finite battery power, which not only limits their operationallifespan but also confines their deployment to locations that allow for easy main-tenance. Given the anticipated global scale of billions of devices, this power limi-tation becomes even more daunting, compounded by the environmental impact ofelectronic waste generated by depleted batteries. An overhaul of current IoT de-sign is urgently needed, one that prioritizes sustainable power solutions to reducedependence on traditional batteries.11.1.1 Evolution of IoTThe concept of the IoT is not new; in fact, its foundations were laid well beforethe term was officially coined in 1999. A major early milestone was the introduc-tion of Radio Frequency Identification (RFID) in the 1980s, which enabled wirelesstracking and identification of objects. This technology, combined with the rapid ex-pansion of the Internet and advances in wireless communication in the late 1990sand early 2000s, paved the way for the modern IoT landscape. As IoT evolved,innovations such as sensor miniaturization and advancements in low-power wire-less communication further fueled its expansion, highlighting the growing need formore efficient, scalable, and interconnected devicesStandardization bodies have played a crucial role in developing communica-tion technologies to ensure IoT interoperability across diverse networks, particu-larly through the evolution of Low Power Wide Area Network (LPWAN) technolo-gies. Within cellular networks, the 3rd Generation Partnership Project (3GPP) in-troduced IoT support with fourth generation (4G) Long Term Evolution (LTE) net-works, alongside technologies like LTE Machine Type Communications (LTE-M)and Narrowband IoT (NB-IoT) for operation in licensed frequency bands. In theunlicensed spectrum, the Institute of Electrical and Electronics Engineers (IEEE)developed the 802.11ah standard, designed to handle higher data rate scenarios insub-1 GHz frequencies. Additionally, proprietary LPWAN technologies such asSigFox and LoRa have further expanded the reach of IoT by offering low-powersolutions that facilitate broader deployment.Despite significant technological advances, the dependence of LPWAN andother IoT devices on finite battery power has emerged as a bottleneck, restrictingboth their lifespan and scalability within these networks. This gap has led to theemergence of Ambient IoT, a new class of IoT devices designed to be wirelesslypowered, envisioned to exist between the complexities and power consumption ofRFID and LPWAN technologies. While commercial solutions from Powercast [12]and Ossia [13] already enable the wireless powering of simple IoT devices, recentstandardization efforts by 3GPP, IEEE, and Bluetooth Special Interest Group (SIG)represent the first global initiative to formalize this technology. In Chapter 3, wedelve into these standardization efforts and analyze how Ambient IoT integrates2Figure 1.1: The design targets set out for Ambient IoT as envisioned by 3GPPin the TSG SA working group 1 study item [3].into the broader technological landscape. Figure 1.1 illustrates the preliminarydesign targets established by 3GPP.1.1.2 Energy HarvestingAmbient IoT devices will be powered through energy harvesting, converting en-ergy from various environmental sources into a usable form to enable continuousoperation. The simplest form of energy harvesting involves capturing existing en-ergy from ambient sources, while dedicated energy harvesting directs energy to adevice for intentional powering. Common ambient sources include solar, thermal,wind, and radio frequency (RF) signals, which are discussed below. The potentialpower available from these sources is detailed in Table 1.1, focusing specificallyon ambient sources, though it is not exhaustive. This exploration will emphasize3energy reliability, as power density varies by source, as illustrated in the table.Light: Photovoltaic cells convert light into electrical power, using both indoor arti-ficial light and sunlight for energy harvesting. The energy harvested depends on thecell’s area and light intensity [14], but increasing the cell size is often limited by thesize constraints of IoT devices. Solar energy, abundant and eco-friendly, is heavilydependent on daylight and affected by weather, such as cloud cover. Indoors, lightis usually only available during occupancy. Photovoltaic cells, also known as solarcells, are commercially available, maintenance-free, and offer predictable energyoutput due to the diurnal and seasonal cycles, though weather can introduce someuncertainty [2].Thermal: Thermal energy can be harvested from sources like body heat or ma-chinery and converted to electrical energy using thermoelectric generators. Whilereliable and durable, these harvesters struggle with low power densities in environ-ments with stable temperatures, as maintaining a significant temperature differenceis crucial for power generation [14]. Power generation ceases when both junctionsreach the same temperature. Although thermal energy sources are unpredictable inambient conditions, they can offer controllability, for example, through the use ofindustrial processes.Wind: Wind energy can be harvested from the environment by converting kineticenergy into electricity [14]. Although micro wind turbines exist, they are oftenbulky and intrusive for IoT devices. However, harvesting wind energy is beneficialfor specific applications, such as providing data on wind speed or direction [15],while indoor harvesting is particularly challenging due to limited airflow.RF: RF energy is widely available and can be harvested from signals transmit-ted by various communication systems. Harvesting is performed using a rectenna,which converts RF signals into direct current (DC) power. The amount of energycaptured depends heavily on the distance from the transmitting source [14]. Therise of wireless communication technologies has increased RF energy availability,particularly in urban environments with substantial infrastructure, such as TV tow-4Source Power Density Predictability ControllabilityLight10µW/cm2−100µW/cm2 (indoors) ✓ ✓100mW/cm2 (solar) ✓ ✗Thermal 15µW/cm2−60µW/cm2 ✗ ✓Wind 20mW/cm2 ✓ ✗Ambient RF 0.2nW/cm2−1µW/cm2 ✓/✗ ✗Table 1.1: Comparison of power density, predictability, and controllability ofvarious ambient energy harvesting sources [1, 2].ers and cellular base stationss (BSs). While ambient RF energy can be predictablefrom constant sources like TV and radio towers, it may also be erratic due to fluc-tuating demand in cases like Wi-Fi [14]. Unlike solar and and wind energy, RFsignals can penetrate physical objects.In powering the next generation of Ambient IoT devices, RF energy harvestingis increasingly favored due to its growing density in urban environments and itsability to utilize existing communication infrastructure. As wireless communica-tion and broadcasting continue to expand, the available ambient RF power densityrises, positioning it as a valuable and reliable power source for Ambient IoT de-vices. Furthermore, the compact design of RF harvesters complements the smallform factor envisioned for these IoT devices [14]. The applications of AmbientIoT are designed to facilitate communication, and simultaneous wireless informa-tion and power transfer (SWIPT) enhances both functionality and versatility byenabling efficient power delivery while preserving communication capabilities inRF energy harvesting [16]. Notably, the harvesting infrastructure for RF signalsclosely resembles that used for communication, as illustrated in Figure 2.3. Al-though RF energy harvesting for Ambient IoT shows great promise, the reluctanceof current standardization efforts to include it in their scope of work raises concernsabout the practical implementation and deployment of such devices.1.2 ContributionsIn this thesis, we explore reliable solutions for powering the next generation of Am-bient IoT devices through RF energy harvesting. While RF energy harvesting forIoT devices is not new, this work’s key contribution lies in evaluating both ambi-5ent and dedicated RF sources for sustainably powering Ambient IoT devices. Ourholistic RF energy harvesting model introduces two key metrics, energy harvest-ing coverage and charging probability, that ensure reliable power delivery. Thesemetrics form the core of our evaluation framework, offering insights into poweravailability across diverse environments. The model is then applied to analyzevarious RF energy sources.To evaluate the potential of powering Ambient IoT devices through ambient RFsources, we analyze pseudo-ambient data generated from real-world measurementsusing autoregressive processes. While this method aids in exploring the feasibilityof Ambient IoT, its ability to realistically simulate ambient RF energy availabilityremains unvalidated. As a result, we focus more on its analytical insights ratherthan its accuracy in reflecting real-world conditions. This analysis also highlightsthe challenges of relying solely on ambient RF sources, especially in environmentslike indoor spaces where energy availability is limited.Beyond ambient sources, we investigate dedicated RF energy sources to en-hance reliability and coverage. By strategically deploying dedicated sources andintegrating multi-antenna systems, we demonstrate significant gains in energy cov-erage and charging probability. Building on the work of [17], which explores ded-icated transmitters without channel state information (CSI), we extend the analy-sis by applying stochastic geometry to investigate different deployment strategies.This approach highlights the potential for repurposing existing infrastructure asdedicated sources for energy harvesting. Numerical results show that effective en-ergy harvesting can be achieved even without CSI, supporting the feasibility ofutilizing dedicated infrastructure to power Ambient IoT devices. Our findings sug-gest that deploying dedicated RF energy sources will be essential to ensure reliableand sustainable powering of Ambient IoT devices.1.3 OrganizationChapter 2 provides a foundational overview of RF energy harvesting, exploring thecharacteristics of RF energy and offering insights into modeling systems designedfor use with Ambient IoT devices. Chapter 3 delves into the current state of Am-bient IoT technology, outlining specific design goals and requirements while ana-6lyzing its place within the broader IoT ecosystem. It also discusses the challengesof powering these devices, from both development and standardization perspec-tives. Chapters 4 and 5 present solutions for powering Ambient IoT devices, withChapter 4 focusing on ambient RF sources, and Chapter 5 investigating how dedi-cated RF sources can offer more predictable and reliable power. Finally, Chapter 6concludes the thesis and highlights potential directions for future research.7Chapter 2Wireless RF Energy HarvestingWireless RF energy harvesting, often referred to as wireless power transfer (WPT)when harvesting from dedicated sources, involves capturing transmitted RF signalsand converting them into usable DC power. This technique presents a particularlyattractive solution for powering Ambient IoT devices, as it enables battery-freefunctionality and facilitates deployment in areas previously not accessible withexisting IoT devices. The pervasive presence of RF energy in digital environments,along with its ability to penetrate various obstacles, positions it as a sustainable andeffective energy source for these devices.Despite its potential, the effective implementation of RF energy harvesting re-quires a deep understanding of system characteristics and requirements, as lowend-to-end power conversion efficiency (PCE) and component losses can signifi-cantly impede performance. This chapter offers a holistic exploration of RF en-ergy harvesting systems, beginning with a review of electromagnetic (EM) wavepropagation. We then examine the key components of these systems and their in-terdependencies, providing insights into modeling approaches that can optimizeperformance. Additionally, we develop models for harvesting power and storingthe resulting energy, which will be utilized in Chapter 4 and Chapter 5 to analyzevarious sources of RF energy.82.1 RF Wave PropagationTo understand the fundamentals of RF energy harvesting, it is essential to first ex-plore the nature of RF energy itself. RF energy refers to the EM energy transmit-ted through radio waves, typically within a frequency range below 300 GHz [18].When a signal is applied to an antenna, it emits waves consisting of oscillatingelectric and magnetic fields that together form the EM field. The propagation be-havior of these waves varies with both frequency and the distance between thetransmitting and receiving antennas.2.1.1 Antenna Operating RegionsTo harness RF energy effectively, it is vital to understand the different operatingregions of a receiving antennas. The area surrounding a receiving antenna can becategorized into near-field and far-field regions, as depicted in Figure 2.1. Theboundary between these regions is approximated by the Fraunhofer distance, givenbydf =2D2λ, (2.1)where df separates the near-field regions (≤ df) from the far-field region (> df).Here, D is the largest dimension of the transmitting antenna and λ is the free-spacewavelength of the EM wave. The wavelength can be calculated asλ =cf, (2.2)where c is the speed of light in free space (approximately 3× 108 m/s) and f thefrequency of the signal. In the far-field region, EM wave propagation is charac-terized by relatively uniform radiation patterns, while in the near-field region, theelectric and magnetic components of the signal interact in a more complex and in-dependent manner [19]. Equation 2.1 provides an estimate of the transition pointwhere this behaviour shifts.9Distance (d)FraunhoferDistance2D2λ0 λ2πInductive RadiativeFar-FieldNear-FieldRF SourceFigure 2.1: Propagation of RF waves in free-space, highlighting the distribu-tion of near-field and far-field regions.Near-field energy harvesting techniques, such as EM induction and magneticresonance, are specifically optimized for short-range applications. In contrast, far-field systems are designed to capture and convert radiated EM waves over longerdistances. The diverse applications and deployment environments anticipated forAmbient IoT, as discussed in Chapter 3, lead us to concentrate our efforts on har-vesting energy in far-field regions. However, understanding the distinctions be-tween near-field and far-field transmission characteristics is crucial, as the systemsand circuits used for harvesting in each are fundamentally different.2.1.2 Propagation ChannelThe process of harvesting RF energy begins with receiving signals and capturingenergy at an antenna. The received power, PRx, in the far-field region can be esti-mated using Friis’ transmission equation given byPRx = PTxGTxGRx(λ4πd)2. (2.3)where PTx and GTx represent the power and gain of the transmitting antenna, re-spectively, and GRx is the gain of the receiving antenna.In this context, path loss refers to the attenuation of EM waves as they propa-gate through space. It is defined as the ratio between the transmit power and receive10power, and is given by,L =PTxPRx. (2.4)Path loss can also be expressed in decibels (dB), with LdB given byLdB = 10log10(L) = 10log10(PTxPRx). (2.5)In its simplest form, path loss can be assessed under the assumption that the sig-nal travels through free-space without obstructions between the transmitter andreceiver, a scenario referred to as free-space line-of-sight (LOS). Free-space LOSpath loss can be estimated using Equation 2.3, with LdB expressed asLFS-LOSdB = 10log10(1GTxGRx(4πdλ)2). (2.6)From this equation, it is evident that path loss increases with the square of thedistance from the transmitting source. In real-world scenarios, path loss rarelyfollows Equation 2.6, as most wireless communication systems operate in complexenvironments where LOS conditions may not apply [20]. In such cases, a generalpath loss model can be employed, where LdB is given byLdB = 10γ log10(dd0)−KdB. (2.7)In this model, γ represents the path loss exponent, while d0 is a reference dis-tance from the transmitter in the far-field region. The constant KdB is dependent onthe average channel attenuation and the characteristics of the transmitting antenna.When fitting a path loss model to data, KdB can calculated as the free-space pathgain at the reference distance d0:KdB = 10log10(GTxGRx(λ4πd0)2). (2.8)While empirical measurements can help determine the values of γ and KdB, calcu-lating them for every environment that network designers encounter is impractical.Thus, 3GPP has standardized several stochastic channel models to evaluate wire-11less systems under general conditions. The various models considered for fifthgeneration (5G) network scenarios include urban macrocell (UMa), urban micro-cell (UMi), indoor factory (InF), and indoor hotspot (InH) environments [4]. Thesemodels are utilized to ensure consistency among simulations for network planningand standardization efforts. In comparison with Equation 2.6, the 3GPP path lossmodels result in significantly different average attenuation by distance, as shownin Figure 2.2.Figure 2.2: Path loss in dB versus distance between a transmitter and receiverfor free-space LOS and 3GPP LOS 5G network models.2.1.3 Shadow FadingPath loss is useful for quantifying the mean signal attenuation as RF waves travel;however, it does not account for smaller fluctuations in the signal. These fluc-tuations can be described by shadow fading, which causes random variations inreceived power due to blockages in the signal path. Because the properties andpositions of these obstructing objects are unpredictable, statistical models are es-sential for characterizing shadow fading.Log-normal fading is a widely used model that has been empirically validated12to accurately represent variations in both indoor and outdoor RF propagation en-vironments [20]. Under this model, we represent the ratio of transmit to receivepower asβdB = 10log10(PTxPRx)(2.9)βdB can be calculated as the average path loss with the addition of a zero-mean,normally distributed shadow fading term. Using Equation 2.7, we this asβdB = 10γ log10(dd0)+KdB+SdB= LdB+SdB.(2.10)In the above, LdB represents the average path loss, while the term SdB ∼N (0,σ2dB)represents the zero-mean, normally distributed shadow fading in dB. The term σdBis the standard deviation the shadow fading. The distribution of βdB is given byp(βdB) =1√2πσdBexp[−(βdB−µdB)22σ2dB]. (2.11)Here, µdB and σdB are the mean and standard deviation of βdB, both in dB. Whilewe have assumed that βdB represents the ratio of transmit to receive power thusfar, it can also be utilized to represent the values of received power, as discussed inChapter 4.2.2 RF Energy Harvesting ReceiverThe RF energy harvesting receiver, so named for its functional similarities to acommunication receiver, is the core component of systems designed to captureRF energy. The architecture of such a receiver depends heavily on the specificrequirements of the target application, which in this thesis are focused on AmbientIoT devices. The receiver can be configured to manage the harvested energy in twoprimary ways, depending on how the energy is utilized:• Harvest-use: In this approach, harvested RF energy is immediately used topower the device, requiring the incident power to exceed the minimum re-13quired for operation; no energy storage element is used. This architecture istypically employed in RFID devices, where backscattered energy is simulta-neously used for powering the device and and communication.• Harvest-store-use: Here, harvested energy is stored future use. Devicesusing this architecture may operate autonomously when the stored energysurpasses a certain threshold, or may wait until prompted to turn on by anexternal source. The harvest-store-use architecture allows for intermittentoperation, such as sensing or transmitting, and provides flexibility [21].This thesis focuses on modelling RF energy harvesting systems for Ambient IoTapplications, emphasizing a harvest-store-use architecture for improved adaptabil-ity and efficiency. Although detailed circuit implementations are beyond the scopeof this work, the emphasis lies in modeling RF energy harvesting systems to assesstheir overall performance in the context of Ambient IoT applicability. A completeharvest-store-use energy harvesting system consists of two main subsystems: therectenna and the power management unit (PMU) shown in Figure 2.3.MatchingNetworkRectifierVoltageMultiplierEnergyStorageLoadRx AntennaRectenna Power Management UnitFigure 2.3: General architecture of a harvest-store-use RF energy harvestingreceiver designed for integration with an Ambient IoT device, shownhere as a general load.2.2.1 RectennaAt the core of RF energy harvesting circuitry lies the rectenna, or rectifying an-tenna, responsible for converting alternating current (AC) signals into usable DCenergy. The rectenna comprises three key components: the receiving antenna,impedance matching network, and rectifier.14Receiving antennaThe receiving antenna is responsible for capturing incident RF signals within aspecified frequency range, and its performance is defined by characteristics such asgain, resonant frequency, and bandwidth. The choice of antenna design is signif-icantly influenced by the RF source being harvested. For uncontrollable ambientRF sources, which inherently possess some randomness as discussed in Chapter 4,an omnidirectional, wide-band antenna is often the preferred choice. In contrast,when harvesting from dedicated transmitting sources with known locations, di-rective antennas can be utilized to improve harvesting efficiency and increase re-ceived power [22]. This thesis assumes that the Ambient IoT device will use asingle antenna for both communication and energy harvesting, consistent with theenvisioned simple device architecture for this technology.Impedance matching networkImpedance mismatches between the RF source at the receiving antenna and therectifier can lead to reflected power, which in turn reduces the power available forrectification. To address this issue, an impedance matching network is employedto maximize power transfer to the rectifier. The design of these networks is contin-gent upon the targeted frequency of the system and may incorporate configurationssuch as π , L, or T sections using reactive lumped components [23]. In RF energyharvesting systems that utilize broadband antennas, it is common to combine mul-tiple matching network sections in series or parallel configurations. However, itis important to note that losses generally increase as the number of componentsrises [24].RectiferThe rectifier plays a crucial role in converting received RF signals into the DCvoltage needed to power the IoT device in question. Typically, rectifiers utilizeSchottky diodes due to their low forward voltage drop and minimal power con-sumption [25]. When designing a rectifier, the objective is to operate effectively atlow input power levels while achieving high PCE. At low power levels, the rec-tifier’s performance is critical for overall system efficiency, significantly impact-15ing the RF to DC conversion process. The PCE of RF-DC rectifiers is influencedby their non-linear characteristics, including the turn-on threshold of diodes andsensitivity to varying power levels. Notably, RF-DC rectifiers typically require aminimum operating threshold between -35 dBm and -25 dBm [26]. This thresh-old is significantly higher than the -130 dBm to -60 dBm range commonly seen ininformation transmission systems [27, 28].2.2.2 Power Management UnitIn harvest-store-use RF energy harvesting receivers, the PMU is responsible forcontrolling the flow of energy from the harvester to the device, effectively actingas a bridge between the rectenna and the energy storage. A typical PMU consistsof a DC-DC converter, an energy storage element, and additional control logic tooptimize the transfer of energy, ensuring efficient power management.DC-DC ConverterThe DC voltage obtained after rectifying the harvested RF energy is generally low,necessitating the use of a DC-DC boost converter to efficiently elevate the voltageand transfer energy to the storage reservoir. In addition to voltage conversion, thePMU incorporates control circuitry to track the harvested energy and optimize itsdelivery to the storage component. While an ideal DC-DC boost converter wouldhave an efficiency of 100%, practical implementations typically achieve efficien-cies between 80% and 90% [29].Energy StorageTo operate effectively, an IoT device must store harvested energy in an appropriateenergy storage element for later use. Traditionally, rechargeable or single-use bat-teries have been the primary choice; however, as discussed in Chapter 1, batteriespresent several drawbacks for the widespread deployment of IoT devices. Alterna-tive options for RF energy harvesting systems include conventional capacitors andsupercapacitors.Capacitors are simple devices consisting of two conductive electrodes sepa-rated by a dielectric material. When a voltage is applied, charge accumulates on the1610−2 10−1 100 101 102 103100102104106CapacitorsBatteriesSupercapacitorsEnergy Density (Wh/kg)PowerDensity(W/kg)Figure 2.4: Ragone plot comparing power density and energy density for ca-pacitors, supercapacitors, and batteries. Inspired by [6], this plot illus-trates that capacitors and supercapacitors exhibit high power density butlow energy density in contrast to batteries.plates, creating an electric field and thus storing energy. Supercapacitors, in con-trast, consist of two electrodes immersed in an electrolyte solution. They store en-ergy both electrostatically and through reversible electrochemical reactions. Com-pared to rechargeable batteries, supercapacitors and capacitors offer longer cyclelife, lower maintenance requirements, and faster charging cycles, making themmore suitable for Ambient IoT applications. Traditional capacitors are often fa-vored for applications requiring the storage of smaller energy amounts [30]. Super-capacitors can store larger amounts of energy due to their higher rated capacitanceand perform better than capacitors in terms of temperature stability and handlinghigh discharge currents [14]. However, supercapacitors also face challenges, in-cluding high leakage currents and self-discharge effects. Leakage current is thecurrent drawn by a fully charged supercapacitor, while self-discharge refers to thegradual loss of stored charge over time, which can reduce measured capacitanceand is influenced by initial charging conditions [31].17The Ragone plot in Figure 2.4 compares capacitors, supercapacitors, and bat-teries based on their power and energy densities. Batteries have higher energydensity, allowing them to store more energy per unit mass. In contrast, capacitorsand supercapacitors offer greater power density, enabling faster charging and dis-charging rates. This means batteries can support longer operational periods but takelonger to recharge. Conversely, systems with high power density, like capacitors,can deliver significant energy relative to their mass and recharge quickly [6]. Thisadvantage is particularly beneficial for Ambient IoT devices, which require shortbursts of energy for intermittent operation while maintaining low overall energydemands.Despite the assocaitede challenges, supercapacitors are advantageous in RFenergy harvesting systems, especially when combined with consistently availableenergy sources and rapid charging mechanisms. While self-discharge remains aconcern, supercapacitors can be more cost-effective and have lifespans of up to 20years in suitable applications [14]. The choice between capacitors and supercapac-itors should be based on the energy storage requirements and the voltage ratingsnecessary for powering an Ambient IoT device.2.3 System ModellingModeling a complete RF energy harvesting system is often overlooked in exist-ing literature and research. It is essential to consider two distinct models in thiscontext. First, we need to address the modeling of the resulting power harvestedat the output of the rectifier. Second, we must examine the efficiency of storingthis harvested energy for later use. Both aspects are crucial for a comprehensiveunderstanding of the system’s performance.2.3.1 Harvested PowerBy modeling the rectenna components, we can predict the converted DC power atits output. Practical RF energy harvesting systems often exhibit non-linear behav-ior, with the efficiency of the RF to DC conversion primarily determined by therectifier circuit. This conversion process is complex due to various losses, leadingto extensive research into models that accurately represent these phenomena while18balancing complexity and accuracy. Three key areas of operation and thresholdsmust be considered when designing such system: 1) sensitivity, 2) PCE, and 3)saturation.The sensitivity is defined as the minimum power required to activate the har-vesting circuit. It is quantified in dBm asPsensin = 10log10(Pmin1mW), (2.12)where Pmin represents the minimum received power level required in watts (W),which is dependant on turn-on voltage of rectifier diodes [32]. While one mightassume that designing systems for extremely low values of Pmin would be straight-forward, a trade-off exists between the sensitivity of the harvester and the rangeof input powers over which operation is feasible [33]. However, when consider-ing path loss, the sensitivity greatly impacts the range at which energy harvestingis possible, as shown in Figure 2.5a for free-space path loss (Equation 2.6) andFigure 2.5b for the 3GPP InH LOS model. Harvesting RF energy under the free-space LOS path loss condition using an energy havrsting system with a sensitivityof -30 dBm is feasible at distances of roughly 40 m from a 33 dBm transmittingsource, akin to a 3GPP Macro-cell BS (see Appendix A), which is roughly doublethe range predicted using the 3GPP InH model for the same transmitting source.The PCE is defined as the ratio of DC power at the output of the rectifier, PDC,to the incident power at the receiving antenna. The PCE of the rectenna is given byηRF-DC =PDCPRx. (2.13)In physical implementations of RF-DC rectennas, the characteristics of ηRF-DC ex-hibit a non-linear increase once the sensitivity threshold is reached, with the PCErising until it reaches a saturation point. Beyond this point, increasing the incidentpower yields no further gains [34]. This saturation occurs due to the rectifier’sbehavior near the diode breakdown region [28].In the literature, several models have been proposed to characterize PCE in RFenergy harvesting systems. The choice of model significantly impacts system de-sign, as it can influence key findings such as charging time of the energy storage,19(a) Free-space path loss (Equation 2.3).(b) 3GPP InH LOS path loss [4].Figure 2.5: Received power (dBm) as a function of distance from varioustransmitting sources assuming GTx and GRx are both 0 dBi.which we will explore in the subsequent section. A simple linear model, whileeasy to implement, can lead to sub-optimal designs, particularly under varying en-vironmental and operational conditions. On the other hand, an overly specializedmodel, tailored to a specific energy harvesting circuit design, may not generalize20well across different scenarios. To model the harvested power, we consider a gen-eration function that accounts for all rectenna components and is dependent on thereceived power and can be expressed asPharv = p(PRx) , (2.14)where p : R+→ R+ is the harvesting function, which we assume to be continuousand non-decreasing [34]. The general form of p isp(x) =0 x ∈ [0,Psensin ) ,ηRF-DC(x) · x x ∈ [Psensin ,Psatin ) ,ηRF-DC(Psensin ) ·Psatin x ∈ [Psatin ,∞) ,(2.15)where ηRF-DC(·) is the PCE of the system, Psensin is the input power sensitivity, andPsatin is the received power at which the energy harvesting circuit saturates.In Table 2.1, we summarize various models for harvested RF power found inthe literature, each based on Equation 2.15. These models are evaluated for theirability to capture sensitivity, saturation, and a non-linear PCE, offering valuableinsights into their overall efficiency and suitability across diverse environmentalconditions.Each model presented in Table 2.1 was designed with reference to a specificapplication of RF energy harvesting and physical rectenna design. However, thisstudy focuses on characterizing the performance of a general energy harvestingsystem for Ambient IoT. We base our comparisons on the data from [7], whichwas selected for its emphasis on optimizing rectenna units for low input power,specifically in the range of -40 dBm to -20 dBm. The rectenna we consider hasa sensitivity of approximately -30 dBm and is designed for harvesting frequenciesaround 900 MHz, with a bandwidth of roughly 35 MHz. A comparison betweenthe energy harvesting models using this data is illustrated for both output power inFigure 2.6a and PCE in Figure 2.6b.In Figure 2.6a, all models exhibit similar performance when fitting the outputpower data across the input power range in the mW domain. However, the sigmoidmodel stands out by achieving the lowest mean squared error. This distinction21Harvesting Model Mathematical Formulation Sensitivity Non-linear PCE SaturationLinear (L) [35]pL(x) = ηL · x, x ∈ [0,∞),✗ ✗ ✗where ηL ∈ [0,1].Constant-linear (CL) [34]pCL(x) ={0, x ∈ [0,Psensin ]ηCL · (x−Psensin ), x ∈ [Psensin ,∞) , ✓ ✗ ✗where ηCL ∈ [0,1].Constant-linear-constant (CLC) [34]pCLC(x) =0 x ∈ [0,Psensin ]ηCLC · (x−Psensin ) , x ∈ [Psensin ,Psatin ]ηCLC · (Psatin −Psensin ) , x ∈ [Psatin ,∞) ✓ ✗ ✓where ηCLC ∈ [0,1].Polynomial (Poly) [36]pPoly(x) = a1x2+a2x+a3 x ∈ [0, inf)✗ ✓ ✓where a1 ≤ 0,a2 > 0 and a3 ≤ 0 are model specific constants.Logistic (Log) [28]pLog(x) =Psatin1+ exp(−a(x−b))−Psatin1+ exp(ab)1− 11+ exp(ab)✗ ✓ ✓where a,b are circuit specific constants.Sigmoid (Sig) [37]pSig(x) =[Psatinexp(−aPsensin +b)(1+ exp(−aPsensin +b)1+ exp(−ax+b) −1)]+, x ∈ (0,∞)✓ ✓ ✓where a,b are circuit specific constants.Table 2.1: Comparison of power harvesting models used in RF energy har-vesting systems literature. The models are characterized for their abilityto model sensitivity, a non-linear PCE, and saturation.is more apparent in Figure 2.6b, where PCE is compared to input power in thedBm domain. The polynomial model closely mirrors the behavior of the linearand constant-linear models, making them difficult to distinguish in the figures. Alimitation of the data from [7] is the lack of values near the saturation point. Thesigmoid model addresses this by incorporating Psatin as a parameter. This non-linearmodel is widely recognized for accurately representing energy harvesting circuits,particularly at low input power levels. In [38], the sigmoid model outperformed thelinear model by slightly underestimating the converted power at low input powerlevels, consistent with the trends observed in Figure 2.6a and Figure 2.6b. Sinceavailable RF energy is typically concentrated near the sensitivity threshold due topath loss, as explored in Chapter 4, capturing the system’s behavior at low input22(a) Output power versus input power in mW.(b) PCE versus input power in dBm.Figure 2.6: Comparison of power conversion models for RF energy harvest-ing with data from a real system in [7] with input power in the milliwattrange.power levels is crucial. This supports our decision to prioritize the sigmoid modelmoving forward.232.3.2 Stored EnergyThe storage of harvested energy in battery-less IoT devices is typically modeledusing the characteristics of capacitors or supercapacitors as previously discussed.For simplicity, we consider the charging process of a conventional capacitor, whileacknowledging the added complexities in modeling leakage with supercapacitors.For a comprehensive discussion on factors affecting capacitance and stored chargein supercapacitors, such as temperature and charging current, one can refer to [39].The energy stored in a conventional capacitor is given byEcap =12CV 2c , (2.16)where C is the nominal capacitance in farads (F) and Vc is the voltage (V) acrossthe capacitor terminals. The power loss due to leakage can be estimated in severalways, one of which involves using an equivalent leakage resistor circuit model.However, we choose to estimate leakage using a leakage constant for its simplicityand generalizability across various physical components in our energy accumula-tion model. Since leakage in a capacitor is proportional to energy, we express theleakage power asPleak = kCV 2c , (2.17)where k is the capacitor leakage constant, proportional to capacitor size and typi-cally very small (e.g., 0.01 for µF range capacitors) [40].While it is common to assume constant voltage charging when evaluating chargeaccumulation in a capacitor (e.g., from a DC power supply), we adopt the methodproposed in [41], which treats the input power to the energy storage element as aconstant over a fixed time interval. The key distinction between constant voltageand constant power charging lies in their operational dynamics; constant voltagecharging maintains a fixed supply voltage, while constant power charging entails ascenario where the supply voltage increases and the current decreases as the capaci-tor voltage rises, thereby ensuring a consistent power delivery to the load. AlthoughRF signals exhibit time-varying characteristics due to factors like shadowing andnetwork activity, we propose an adaptation to the constant power model, assum-ing the input power remains constant over short intervals. During these intervals,24Charging ON Charging ON ChargingVminVfullVcap = 0TimeFigure 2.7: Voltage plot illustrating the charging and discharging cycles of anenergy storage capacitor in an Ambient IoT device. The device expe-riences multiple charging phases, followed by active operation periodswhere the capacitor discharges (e.g., during ON states).charge accumulation is modeled using the constant power approach. This approachallows us to examine fluctuations in charging power, which have not typically beenconsidered in works when charging power is assumed to be constant [42, 43]. Un-der this model, the power supplied to the storage capacitor at time t is given byPharv(t) =Vharv(t)Iharv(t). (2.18)Here, Vharv and Iharv are the time-varying harvesting voltage and current being pro-vided to the energy storage element. Mishra et al. showed that the time required tocharge a capacitor from empty (i.e., Vc = 0) to a voltage V ′c with constant power Pis given by [41]T(V ′c ,P)=12RC(2CV ′cA−CV ′c+ ln(A+CV ′cA−CVc)), (2.19)where A =√CV ′c +4CRP and R is the equivalent series resistance (ESR) of thecapacitor. For the case of a capacitor having initial voltage (i.e., Vc ̸= 0), the timeto charge a capacitor to a new voltage can be calculated using Equation 2.19 asτC = T(V ′c ,P)−T (Vc,P) . (2.20)Capacitors are rated for a maximum charging voltage, beyond which they deteri-25orate due to dielectric breakdown and overheating. In our model, we assume thePMU will regulate charging to prevent the capacitor from exceeding this limit dur-ing constant power charging. Additionally, there will be a minimum voltage whichis required for the Ambient IoT device to operate, and below this point, the PMUwill switch the device off and begin re-charging. This is related to the minimumenergy required to preform a task, which we calculate asEON = Pminτmin. (2.21)In the above, Pmin is the power consumed by the Ambient IoT device during thisperiod of being ON, which has a duration of τmin. Therefore, the minimum voltagerequired for a device to stay on can be calculated with Equation 2.16. An exampleof capacitor voltage during charging (energy harvesting) and on-time (discharging)intervals is shown in Figure 2.7, where it is evident that a capacitor may not startwith Vc = 0 at the beginning of a charging interval.Using Equation 2.19 and Equation 2.20, we can solve for a capacitors newvoltage after it has been charged at a fixed, harvested power, Pharv, for time intervalτ asV ∗c (t+ τ) = max(arg minVc(t+τ)(τ− [T (Vc(t+ τ),Pharv(t))−T (Vc(t),Pharv(t))]) ,Vmax).(2.22)Energy Evolution ModelThe stored energy within a capacitor will evolve over time based on the previouslymodelled harvested power. Taking into account the leakage of the capacitor model,we assume that energy is accumulated within the storage element over time asEstored(t+ τ) =Estored(t)− τPleak, Pharv(t) = 0,Estored(t)+ηDC-DC 12CV 2c (t+ τ), Pharv(t)> 0, (2.23)26where Pharv is defined in Equation 2.14, Pleak is defined in Equation 2.17, and Vc(t+τ) is determined according to Equation 2.22. To incorperate losses experiencedat the voltage multiplier in our model, we set ηDC-DC to be its efficiency, whichassume to be a constant [44].27Chapter 3Ambient IoTAmbient IoT has been conceptualized to describe a class of devices that can becharged autonomously, without human intervention. The 3GPP defined this emerg-ing class of IoT devices in Release 18, characterized by ultra-low complexity andultra-low power consumption [45]. In this chapter, we provide an overview of theAmbient IoT landscape, outlining the general device characteristics that we aim tosupport with reliable powering solutions in the subsequent sections of this thesis.We begin by discussing the current standardization efforts surrounding thistechnology, followed by an overview of its unique requirements and objectives.Next, we expand on the discussion from Section 1.1.1 regarding existing IoT com-munication protocols and other relevant technologies, positioning Ambient IoTwithin this broader context. Finally, we explore the envisioned use cases and op-erational scenarios for these devices, setting the stage for future work on reliablecharging in Chapters 4 and 5.3.1 Standardization of Ambient IoTAmbient IoT has gained traction among numerous organizations and industry groupsworking towards its adoption. The 3GPP, responsible for cellular network stan-dards; the IEEE, which sets Wi-Fi standards; and the Bluetooth SIG, overseeingBluetooth technology, are collaborating with IoT experts to drive the standardiza-tion of Ambient IoT. This section explores the current status of Ambient IoT within28each group, providing an overview of their ongoing efforts and scope.3GPP3GPP has standardized numerous wireless communication technologies, such asGlobal System for Mobile communication (GSM), and LTE, as well as several IoT-related technologies, including LTE-M and NB-IoT. The standardization efforts onAmbient IoT within 3GPP have been ongoing, with several Technical SpecificationGroups (TSGs) involved in the process. The integration of Ambient IoT into cellu-lar networks offers the advantages of utilizing wide-area global coverage, existinginfrastructure that could operate as communication nodes or power supplies, andthe licensing of high-quality sub-1 GHz frequency bands that offer minimal prop-agation losses.Ambient IoT has been studied by two key groups within 3GPP: the TSG onSystem Aspects (SA), specifically in working group 1 (SA1), and the TSG onRadio Access Networks (RANs). The work in SA1 began in February 2022, result-ing in a technical report that outlined over 30 use cases, traffic scenarios, and initialservice requirements [3]. Based on the SA1 report, Ambient IoT was approved asa RAN study item in September 2022, with a focus on network topologies anddeployment scenarios [46]. Work in RAN working group 1 (RAN-1) commencedin February 2024 and will be reviewed in December 2024 for potential conversioninto a work item. Ambient IoT is currently considered one of the most promisingtechnologies within 3GPP, as evidenced by the significant resources dedicated toit in recent meetings.IEEEThe widespread adoption of IEEE Wi-Fi standards offers a strong foundation fordeploying Ambient IoT devices, utilizing the extensive availability of WirelessLocal Area Network (WLAN) infrastructure and unlicensed frequency bands forseamless connectivity and integration. Wi-Fi standards such as 802.11b/n havebeen applied in industrial IoT applications, while the 802.11ah standard was intro-duced to accommodate a larger number of connections and support lower-powerdevices [8]. However, none of these standards currently provide native support for29the integration of Ambient IoT devices, but they can serve as the basis for whichthe technology is standardized on.The IEEE Technical Interest Group (TIG) on Ambient IoT was established inMay 2022, initially under the name ambient power-enabled devices (AMP). Thisgroup conducted extensive research into use case scenarios and design targets, withmultiple companies contributing demonstrations and feasibility studies. In March2023, a study group on AMP was launched, leading to the approval of a ProjectAuthorization Request (PAR) and Criteria for Standards Development (CSD) inNovember 2023. By March 2024, the 802.11bp task group was formed to enhance802.11 WLANs to support AMP devices [47]. While 3GPP has focused on broaddeployment of Ambient IoT devices across various environments, IEEE’s AMPtask group has prioritized short-range technologies, particularly in object identifi-cation.BluetoothThe Bluetooth SIG is actively working to integrate Bluetooth technology into theAmbient IoT ecosystem. This initiative involves enhancing Bluetooth Low En-ergy (BLE) for applications in asset tracking, sensor networks, and location ser-vices. The SIG is particularly focused on real-time location systems and assettracking, aligning with the priorities of IEEE AMP. This group has assessed thetotal addressable market for Ambient IoT to exceed 10 trillion devices [48]. Addi-tionally, the Bluetooth SIG has standardized a BLE protocol for battery-less elec-tronic shelf labels, which is envisioned as a foundational element for BLE in Am-bient IoT [49]. BLE technologies are energy efficient only over shorter ranges,leading the SIG to prioritize close proximity object identification and sensing.3.2 Existing IoT SolutionsInnovations in RFID and LPWAN technologies have significantly improved con-nectivity while reducing energy demands, laying a robust foundation for AmbientIoT. This emerging technology is specifically designed to facilitate denser devicedeployments compared to existing IoT solutions by leveraging small form factorsand extended communication ranges relative to RFID. By aiming to fill the gaps30RangeTransmittingPowerConsumptionLowMed.HighRFIDAmbient IoTUnlicensed LPWANLicensed LPWANCost: $ $$$1m 10m 100m 1km 10kmFigure 3.1: Positioning of Ambient IoT compared to RFID and existing LP-WAN technologies for IoT. Ambient IoT is envisioned to exist betweenthese sets of technologies regarding power consumption during a trans-mission, range, and device cost [8, 9].left by current solutions, Ambient IoT is intended to coexist with cellular IoT andLPWAN technologies, which excel in long-range communication and cost effi-ciency. By harnessing environmental energy, Ambient IoT enhances energy au-tonomy and sustainability, thereby complementing existing solutions and reducingreliance on traditional power sources. As illustrated in Figure 3.1, Ambient IoToccupies a unique position within the broader device landscape.3.2.1 RFIDRFID is a low-complexity communication technology that utilizes RF signals forcontactless interaction through backscattering. In this process, RFID devices, com-monly referred to as tags due to their simplistic design, reflect signals from areader to convey unique identifiers. However, traditional RFID systems are limitedby their short range and typically operate on a harvest-then-transmit architecture,which does not allow for energy storage for later use. It has been shown that RFIDcan greatly benefit from incorporating RF energy harvesting, a method proven to31extend its effective range [50].RFID tags generally feature simple hardware, including basic rectifiers andsmall antennas, enabling them to reflect an ID without performing significant pro-cessing or autonomous functions [51]. Despite these limitations, RFID finds pri-mary use cases in inventory management, access control, and asset tracking, show-casing its versatility across various applications. RFID provides a baseline for thecomplexity and capabilities envisioned for low-power consumption Ambient IoTdevices.3.2.2 Unlicensed LPWANUnlicensed frequency bands enable technologies such as Wi-Fi, Bluetooth, LoRa,and Sigfox to operate without regulatory licenses. LPWANs are characterized bylow-power, low-cost deployments that utilize device-triggered operation, allowingdevices to remain inactive until data transmission is required. Among these, LoRa,supported by the LoRa Alliance, facilitates scalable and cost-effective deploy-ments, while Sigfox provides a turnkey solution for low-power connectivity [52].Notably, a Sigfox network can support over one million devices, whereas LoRa ac-commodates approximately 100,000 devices [53]. Despite these advantages, bothtechnologies face challenges such as limited bandwidth and increased complexity,which can hinder broader adoption.Unlicensed LPWAN technologies also include those under the IEEE Wi-Fi um-brella, with IEEE 802.11ah emerging as a notable player in this landscape. Thisstandard offers low implementation costs and is widely deployed in consumer de-vices [53]. However, a primary trade-off with unlicensed LPWAN is the lack ofguaranteed reliability. These networks share the spectrum with other users, makingthem susceptible to interference and congestion, particularly in high-traffic areas.Additionally, regulatory constraints, such as duty cycle limits, further restrict trans-mission time, impacting real-time communication [9]. Despite these challenges,unlicensed LPWAN remains a vital component of IoT ecosystems, especially inscenarios where cost and flexibility are paramount.323.2.3 Licensed LPWANLicensed LPWAN solutions, such as NB-IoT and LTE-M, operate within tightlycontrolled spectrums, requiring telecom operators to purchase access. These net-works leverage existing LTE infrastructure, which provides enhanced service qual-ity and reduced latency, making them suitable for mission-critical applications thatdemand secure and reliable connections [52]. Specifically, NB-IoT, developed by3GPP in LTE Release 13, is designed for wide-area coverage, reduced power con-sumption, and extended device battery life — essential features for many IoT ap-plications. In contrast, LTE-M offers higher data rates and supports voice commu-nications, enabling more complex applications while still maintaining low powerusage. However, both solutions incur higher deployment costs due to hardwarecomplexity and the necessity of SIM cards [9]. Additionally, both NB-IoT andLTE-M require periodic device synchronization, which can further increase bat-tery consumption and operational costs [9].A significant challenge for licensed LPWAN solutions is the coverage gapbetween urban and rural areas. While cellular networks predominantly focus ondensely populated regions with established infrastructure, rural areas often remainunder served. This limitation makes licensed technologies less suitable for applica-tions like agriculture or environmental monitoring that require wide-area connec-tivity [52]. Despite these challenges, licensed solutions excel in delivering reliableconnections and consistent data transmission for applications in smart cities, util-ities, and healthcare. Overall, the combination of deployment complexity and au-thentication overheads impacts the cost-effectiveness of licensed solutions in IoTapplications.3.3 Related TechnologiesIn addition to IoT communication protocols, various related technologies can en-hance the development and deployment of massive IoT networks, furthering theobjectives of Ambient IoT. Below, we examine three of these technologies.333.3.1 BackscatteringBackscattering is a low-complexity communication technique that enables IoT de-vices to achieve ultra-low-power data transmissions. Rather than generating theirown signals, these devices reflect and modulate existing RF signals, such as thoseemitted by nearby RFID readers, to transmit data. This method allows for commu-nication without requiring an active transmitter, making it highly efficient for low-power applications. However, implementing backscattering presents challenges,primarily the need for a suitable carrier wave. Existing network elements oftentransmit waveforms that are incompatible with the simplistic nature of backscatter-ing, necessitating the development of new signal sources.Ambient Backscatter Communication (AmBC) has emerged as a technologyclosely aligned with Ambient IoT, where backscattering devices utilize signalsbroadcast from existing network elements, such as cellular BSs or Wi-Fi accesspointss (APs) [54]. This approach enables devices to modulate and reflect incom-ing RF signals, a principle that is foundational to RFID technology. In the contextof Ambient IoT, the simplest devices are expected to incorporate backscatter ca-pabilities, allowing them to leverage incident signals or carrier waves for energyharvesting when communication is unnecessary or when stored energy is insuffi-cient [3].3.3.2 SWIPTSWIPT is a technique that enables the concurrent transmission of both data andenergy through a single RF signal, allowing devices to harvest power while receiv-ing information. It builds on RF energy harvesting by leveraging the dual purposeof RF signals for both communication and WPT. From a powering perspective,SWIPT can function using either time-switching or power-splitting methods.In the time-switching approach, a device alternates between harvesting energyand receiving information, as demonstrated in [55], where interference is exploitedfor energy harvesting, and stronger signals are reserved for data transmission. Thisarchitecture, known as the co-located receiver architecture, uses the same antennafor both energy harvesting and information reception. Time-switching can involveseparate signals for energy and data or a single signal processed at the receiver [16].34Alternatively, power-splitting divides a signal’s power into two streams — one forenergy harvesting and the other for information detection [56]. In this method,energy harvesting and information detection occur simultaneously.SWIPT has been widely applied to IoT network studies, often under the broaderumbrella of wireless sensor networks (WSNs), which typically describe industrialIoT applications enabling uninterrupted connectivity in commercial environments[57]. Although the reliability of energy harvesting is considered an implementa-tion challenge for Ambient IoT, rather than part of its communication protocols,SWIPT could still be relevant. In fact, RFID exemplifies SWIPT, as it harvestspart of the incoming signal’s power for modulation, which is then reflected throughbackscattering due to the harvest-use architecture.3.3.3 Cognitive RadioThe limited availability of licensed spectrum presents a significant challenge tothe widespread deployment of 5G and beyond IoT systems. Cognitive radio of-fers a solution by enabling more flexible and efficient use of network resources.This technology allows wireless communication networks or individual nodes todynamically adjust their transmissions to avoid interference, based on real-timeawareness of the radio environment. By continuously adapting transmission pa-rameters and using spectrum-sharing techniques, cognitive radio optimizes spec-trum utilization and enhances communication efficiency in wireless networks [58].Cognitive radio encompasses a range of techniques aimed at improving re-source use, as outlined in [59]. For power-limited IoT devices, re-transmittingsignals multiple times can significantly impact battery life. Cognitive radio canmitigate this by controlling transmit power, allowing devices more prone to inter-ference or packet loss to adjust their transmission power based on network con-ditions for improved signal reception. Another approach is for IoT networks toopportunistically access multiple channels to reduce interference, particularly inscenarios such as inventory management, where communication with a large num-ber of devices simultaneously is essential. In addition to resource management,cognitive radio facilitates large-scale global deployment. Cognitive radio-equippednetwork elements can adapt to different spectrum regulations in various countries35by dynamically adjusting their communication frequency bands [58]. This flexibil-ity makes cognitive radio a valuable approach for Ambient IoT, offering potentialsolutions in areas like interference management, global deployment, and powerefficiency.3.4 Ambient IoT OverviewAmbient IoT is envisioned as a versatile technology with broad applications acrossvarious fields. It is designed to support communication protocols that facilitate bothactive transmissions and backscatter communication, with a focus on simplicityand low power consumption. These protocols aim to reduce complexity whileextending device lifespans by incorporating longer duty cycles that optimize energyharvesting. This autonomy minimizes the need for frequent battery maintenance,making Ambient IoT particularly suited for deployment in remote or resource-limited environments. In this section, we summarize the current design targets andrequirements for Ambient IoT in ongoing standardization efforts, as well as theenvisioned applications for this technology.3.4.1 Design Targets and RequirementsThe current vision for Ambient IoT involves two primary types of devices: backscat-ter devices for low-power applications like inventory tracking, and active devicesfor use cases that require greater reliability and service guarantees, though withslightly higher power consumption. Within the 3GPP framework, these are cate-gorized as device types 1 and 2 [46]. Backscatter devices are expected to maintaina complexity level similar to RFID, while active devices are designed to consumesignificantly less power than NB-IoT systems [3], positioning them between RFIDand LPWAN technologies in terms of performance and power requirements.The specific communication bands for Ambient IoT are still under considera-tion, with discussions focusing on sub-1 GHz frequencies, as explored by 3GPPand IEEE AMP [46, 47]. Early studies indicate that Ambient IoT will leverageultra-low-power communication and RF energy harvesting, resulting in reducedcosts and complexity when compared to LPWAN technologies [3]. Ambient IoTdevices are expected to operate intermittently, powered solely by harvested energy,36Parameter 3GPP [46, 60] IEEE AMP [61]Power consumptionDev. 1: ∼ 1 µW< 1mWDev. 2: ≤ 1mWCommunication techniquesDev. 1: Backscatter BackscatterDev. 2: Active transmitter Active transmitterCommunicaiton range50 m - 100 m (outdoor) 10 m - 30 m (backscatter)10 m - 50 m (indoor) < 200 m (active transmitter)Device density150 devices / 100 m2 (indoor)Not specified20 devices / 100 m2 (outdoor)Table 3.1: Current state of Ambient IoT device and network design targetsfor 3GPP and IEEE standardization efforts.which eliminates the need for batteries and significantly reduces maintenance re-quirements. The goal, as outlined by 3GPP, is for these devices to have a lifespanof at least 10 years [3].Peak power consumption for Ambient IoT devices is projected to range fromunder 10 µW for backscatter devices to around 1 mW for those requiring activetransmissions [3]. Their resilience to extreme conditions—such as high temper-atures, pressure, or radiation—makes them ideal for deployment in environmentswhere traditional battery-powered devices may struggle to operate reliably. Thecurrent design targets, as outlined by 3GPP and IEEE, are presented in Table 3.1,though Bluetooth has yet to release finalized requirements or detailed design spec-ifications.3.4.2 ApplicationsInitially, over 30 Ambient IoT deployment scenarios were detailed by 3GPP. In TR38.848, these were refined into four representative use cases with similar require-ments, reducing the total for the RAN study [46]. These use cases — inventory,sensors, positioning, and command — apply to both indoor and outdoor environ-ments. Examples of application-specific scenarios are shown in Table 3.2.3.5 Challenges in Device PoweringPowering Ambient IoT devices presents several challenges, especially since orga-nizations like 3GPP, IEEE, and Bluetooth SIG have opted not to establish standard-37Representative use case Example application scenariosInventoryDynamic inventory controlReal-time asset trackingSupply chain managementSmart asset utilizationManufacturingRetailSensorsSmart home automationMachine room monitoringPrecision agricultureEnergy management & smart gridsWildfire detection and monitoringPublic infrastructure monitoringPositioningAsset localizationWarehouse item trackingInteractive navigation solutionsCommandMedical equipment managementElectronic shelf labelsAutomated agriculture control systemsHealthcare facility securityTable 3.2: 3GPP representative use cases for Ambient IoT, along with appli-cation scenarios for each, based on initial use cases from TR 28.840 [3].ized energy harvesting methods at this stage. Currently, charging Ambient IoT de-vices is viewed as an implementation issue rather than a standardized requirement.Consequently, critical aspects such as charging latency and energy harvesting ef-ficiency lack formal technical guidelines. While 3GPP has loosely proposed a(re)charging time of around 10 minutes, this figure mainly serves as a reference fordefining communication duty cycles rather than energy harvesting [60].Given that Ambient IoT is still in the research and discovery phase, the re-quirements and design objectives for these devices will likely evolve before formalstandards are established. As the needs for energy harvesting and reliable poweringcontinue to shift, several challenges must be addressed. These include ensuring astable and sufficient wireless power supply, achieving efficient energy conversion,maintaining compatibility with existing infrastructure, and aligning power solu-tions with emerging Ambient IoT communication protocols. In the absence ofofficial specifications for power performance, exploring various reliable powering38options is essential. In subsequent chapters, we will assess different RF energysources to determine their effectiveness in meeting the power needs of AmbientIoT devices with minimal disruption to future standardization processes.39Chapter 4Harvesting from Ambient RFSourcesThe successful standardization and large-scale deployment of Ambient IoT devicesrely on securing a reliable power source through energy harvesting. In Chapter 2,we examined the characteristics and modeling of a general RF energy harvestingsystem for powering Ambient IoT devices, though the analysis assumed an un-specified energy source. In real-world environments, a diverse array of RF energysources, primarily designed for communication, surrounds us. Ambient RF energyharvesting harnesses the abundant energy generated by cellular networks, Wi-Fi,and other wireless technologies to serve as a power source. As these technologiesproliferate, so does the ambient RF energy available. However, the unique require-ments of Ambient IoT, including the massive deployment of low-cost, low-powerdevices, raise an important question: “Can we harness these existing RF energysources to power this new generation of devices?”In this chapter, we explore the various sources and availability of ambient RFenergy in the context of powering Ambient IoT devices. We will then apply theanalytical model from Chapter 2 to assess the feasibility and effectiveness of har-vesting this energy for powering Ambient IoT devices.40Band Frequency (MHz) Transmitting Sources CategoryDTV 450–650 TV towers staticLTE/GSM 900 700–900 BSs, mobile phones dynamicLTE/GSM 1800 1700–1900 BSs, mobile phones dynamicWi-Fi 2450 APs, mobile phones dynamicTable 4.1: Frequency bands of interest for ambient RF energy harvesting,highlighting the main transmitting sources for each band. The sourcesare categorized by type (static or dynamic) to illustrate potential contrib-utors to ambient RF energy availability.4.1 Ambient RF Energy SourcesThe evolution of communication technologies and the push toward sixth gener-ation (6G) wireless networks have driven the global deployment of BSs, APs,and other communication infrastructure, significantly expanding network cover-age. This growth has led to a denser RF signal environment and introduced newfrequency bands, thereby increasing the availability of ambient RF energy. Ambi-ent RF sources can be categorized as static or dynamic. Static sources, like TV andradio towers, provide a continuous supply of RF energy, while dynamic sources,such as Wi-Fi APs and cellular BSs, deliver time-varying energy due to fluctuationsin transmit power and user demand.For the purpose of this thesis, we begin by exploring ambient RF sources oper-ating below 3 GHz. Higher frequencies suffer from increased attenuation, resultingin greater propagation losses between the transmitting source and the energy har-vesting system’s receiving antenna, shown by the frequency dependency in Equa-tion 2.6. In contrast, lower frequencies, with their longer wavelengths, are moreeffective for transmitting energy over greater distances, which is crucial for reliableenergy harvesting in real-world environments.Gu et al. identified four key RF bands suitable for energy harvesting: digital TV(DTV), 900 MHz and 1800 MHz mobile communication bands, and Wi-Fi [62].These bands, summarized in Table 4.1, present practical opportunities for energyscavenging.Ambient RF energy typically exhibits significant fluctuations, particularly fromdynamic sources, due to factors such as shadowing, transmitter mobility, and net-41Figure 4.1: Ambient RF energy profile example in Boston, MA, showingthe incident RF power along with the general variation trend and meanpower levels [10].work activity, as demonstrated in the example from Boston, MA, in Figure 4.1.There is increasing interest in harnessing ambient RF energy to power IoT devices;however, many studies tend to oversimplify the available ambient energy. For ex-ample, the authors in [63] and [43] explore harvesting from ambient power levelsbut assume these levels remain constant throughout their analysis. This assumptionoverlooks the challenges of powering large-scale Ambient IoT deployments, whichrequire dispersed device locations and reliable energy harvesting. In this chapter,we focus our analysis on time-varying ambient sources.4.1.1 Capturing EnergyAmbient RF signals are commonly represented either as power density levels (dBm/cm2)or as input power measured at a receiving antenna (dBm). The total available powerover a channel, ranging from fl to fh, can be expressed asPch =fh∫flPRF ( f )d f , (4.1)where PRF ( f ) represents the received power at frequency f . For ambient sourceswith measurements given as a power spectral density, the spectral density can be42calculated asPRF( f ) = D( f ) ·Ae, (4.2)where D( f ) is the measured power density at frequency f in units of dBm/(Hz·cm2).Ae is the effective area, or effective aperture, of the receiving antenna given byAe = G( f0)λ024π. (4.3)In the above, f0 is the centre frequency of the receiving antenna, λ0 is the free-spacewavelength at the centre frequency, and G( f0) is the receiving antenna gain at f0.While power density measurements are typically provided over larger bandwidths,we approximate the effective area as constant across all frequencies fl to fh in themeasurement range.4.1.2 Measurement CampaignsSeveral measurement campaigns have been conducted worldwide to evaluate theavailable power from existing RF energy sources across different environments.We focus on sub-1 GHz mobile communication bands, such as LTE and GSM-900, as they offer a balance between moderate ambient power levels and reducedattenuation due to the lower frequencies [62]. Additionally, the sub-1 GHz bandsalign with the communication goals of Ambient IoT discussed in Section 3.4.1, as-suming the use of a single antenna for both energy harvesting and communication.We present a summary of three surveys that collected statistical data on thefrequency bands of interest, as illustrated in Table 4.2. The first source conductedmeasurements in Montreal, Canada, where the authors reported the mean and stan-dard deviation of measurements taken in 22 urban and eight rural areas over severaldays [62]. The second dataset was gathered in Turkey, where measurements weretaken at eight different locations (A to F), encompassing a combination of indoor,semi-indoor, and outdoor settings in close proximity [64]. The third source, basedin Italy, involved measurements at a single urban, semi-urban, and rural location,each characterized by its relative distance to network infrastructure such as cellularBSs [65].The measurements reported differ based on the methods used for capturing43Location Center Frequency (MHz) RBW (MHz) µ (dBm) σdB (dB)Canada [62]Urban850 5-43.07 12.65Rural -48.77 7.89Turkey [64]Indoor - A900 20-42.4 3.22Indoor - B -42.34 3.26Indoor - E -31.85 1.79Semi-indoor - A900 20-34.11 2.57Semi-indoor - B -26.11 2.75Semi-indoor - E -21.95 3.52Outdoor - A900 20-21.67 3.64Outdoor - B -17.49 2.86Outdoor - C -19.88 3.35Outdoor - D -11.31 2.10Outdoor - E -17.96 2.58Outdoor - F -6.20 2.10Outdoor - G -39.51 1.88Outdoor - H -16.68 2.72Italy [65]Urban9003 (asm)-23.74∗ 2.93∗Semi-urban -29.81∗ 4.60∗Rural -55.01∗ -0.39∗Note: ∗ - Measurements in Italy were provided as linear mean and standard deviation. These wereconverted to mW, 10,000 independent samples were generated from the mW normal distribution, thenconverted to dBm to obtain the dBm mean and standard deviation.Table 4.2: Ambient RF measurements for sub-1 GHz communication bandsacross three locations: Montreal, Canada; Isparta, Turkey; and Parma,Italy. The table presents the distribution of measured ambient RF energyin these regions.energy. Specifically, the resolution bandwidth (RBW) of the spectrum analyzer wasreported in [62], and the receiving antenna specifications were described in [64].However, [65] does not specify the RBW. The reported data was based on theintegration of measurements across the entire GSM-900 band, which in Turkey isthe E-GSM band which have uplink and downlink bands spanning 880 MHz to960 MHz [66]. Therefore, we assume that the measurements contain power valuesin 80 MHz (see Figure 2 in [65]).It is evident that the values in Table 4.2 vary significantly. The high valuesobserved outdoors in Turkey (e.g., Outdoor - F) result from high-traffic areas, aphenomenon also noted in campaign carried out in London [67]. Furthermore, thevarying bandwidths considered for harvested energy make comparisons betweenthese locations extremely challenging, as there have been substantial differences in44how these values were reported.While several additional surveys on ambient RF measurements exist beyondthose included in our analysis, such as those by Pin˜uela et al. and Muncuk et al.,we have specifically focused on studies that report the mean and standard deviationof ambient RF power over time. Although Pin˜uela et al. is a widely referencedsource in this field, their measurements conducted in London involve summing thespectral peaks across an entire frequency band at a single point in time and donot provide the necessary statistical data for our analysis [67]. Similarly, Muncuket al. examined available power over an extended period but failed to report themean or standard deviation, making it challenging to understand the distribution ofthe received power over time [10]. Consequently, we have restricted our analysisto the studies presented in Table 4.2, which offer the mean and standard deviationof measurements required for our analysis.4.2 Data GenerationThe measurements presented in Table 4.2 provide insights into the mean and stan-dard deviation of ambient RF power. However, our goal is to gain a deeper under-standing of the actual trends in the resulting ambient power signals. In this section,we outline the methodology used to simulate ambient RF power data for evaluatingRF energy harvesting solutions across various environments. We assume that theambient RF power measurements follow a log-normal distribution, as introduced inSection 2.1.3. Additionally, we incorporate temporal correlation between samplesusing an autoregressive process, ensuring that the received ambient power variessmoothly over time to reflect gradual changes in network traffic, mobility, and pathlosses.Log-Normal DistributionThe received power in dBm following a normal distribution is mathematically de-scribed asPRx ∼N(µdBm,σ2dB), (4.4)where N(µdBm,σ2dB)represents the normal distribution characterized by an av-erage received power in dBm of µdBm and standard deviation in dB of σdB. The45Location µ (dBm) µdBm (dBm) σdB (dB)Canada [62]Urban -43.07 -51.07 12.65Rural -48.77 -51.88 7.89Turkey [64]Indoor - A -42.4 -42.92 3.22Indoor - B -42.34 -42.87 3.26Indoor - E -31.85 -32.01 1.79Semi-indoor - A -34.11 -34.44 2.57Semi-indoor - B -26.11 -26.49 2.75Semi-indoor - E -21.95 -22.57 3.52Outdoor - A -21.67 -22.33 3.64Outdoor - B -17.49 -17.90 2.86Outdoor - C -19.88 -20.44 3.35Outdoor - D -11.31 -11.53 2.10Outdoor - E -17.96 -18.29 2.58Outdoor - F -6.20 -6.42 2.10Outdoor - G -39.51 -39.66 1.88Outdoor - H -16.68 -17.05 2.72Italy [65]Urban N/A -23.74 2.93Semi-urban N/A -29.81 4.60Rural N/A -55.02 0.39Table 4.3: Updated ambient RF measurements from Table 4.2, featuring ad-justed dBm means calculated using Equation 4.5 and replacing the pre-viously reported linear means under the assumption of a log-normal dis-tribution.notation indicates that the received power PRx is a random variable that follows anormal distribution. The measured ambient power levels from Canada and Turkeypresented in Table 4.2 contain the linear mean (µ) and standard deviation in dB(σdB). Following [20], the conversion between the linear mean and dBm mean forthese locations is given byµdBm = 10log10 (µ)−σ2dB2ξ 2, (4.5)with ξ = 10/ ln(10). We present the measurement data with adjusted means inTable 4.3.Correlated SamplingIn real-world environments, measured RF power does not fluctuate arbitrarily butshows some correlation over time, following patterns of network traffic and user46mobility [68]. Instead of sampling ambient power as an independent and identi-cally distributed (i.i.d.) process, we incorporate temporal correlation between sam-ples. To simulate this behavior, we apply a first-order autoregressive (AR(1)) pro-cess, which introduces a degree of time-dependence between successive measure-ments [69]. The general form of an AR(1) process is,xn = α+ p · xn−1+ yn, yn i.i.d.∼ N (0,σ2y ). (4.6)In the equation above, α represents a constant term introducing the mean aroundwhich the process fluctuates, p denotes the correlation between observations xn andxn−1, and yn accounts for innovation of the process, which we require to be inde-pendent and identically normally distributed with a mean of zero and a variance ofσ2y . The theoretical mean and variance of xn are given byE[xn] =α1− p (4.7)Var(xn) =σ2y1− p2 . (4.8)Therefore, in order to simulate the received power as an AR(1) process with adesired mean µdBm and variance σ2dB, we can usePRx[n] = µdBm(1− p)+ p ·PRx[n−1]+PU, n = 1, · · · ,N (4.9)PUi.i.d.∼ N (0,σ2dB(1− p2)).In the above, N represents the number of samples we simulate. The coefficient p,0≤ p< 1, controls the degree of correlation between consecutive samples. A valueof p = 0 indicates that the power values are drawn from an i.i.d. random samplingprocess. An example of the received power generated using Equation 4.9 is illus-trated in Figure 4.2 for three distinct cases of p = 0.1, 0.5 and 0.9, representinglow, medium, and high correlation, respectively.47(a) Low correlation (p = 0.1). (b) Medium correlation (p = 0.5).(c) High correlation (p = 0.9).Figure 4.2: Generated 1,000 samples following an AR(1) process with corre-lation coefficient p. The data is based on the mean and standard devia-tion of the measurements from the Canada urban location in Table 4.3.4.3 Performance AnalysisThe generated pseudo-ambient power data can be utilized to evaluate the perfor-mance of an Ambient IoT device equipped with an RF energy harvesting system.In the following section, we outline two performance metrics that will be used forthis analysis.4.3.1 Harvesting CoverageFollowing the discussion of sensitivity in Section 2.3.1, we consider the energyharvesting probability as the probability that an Ambient IoT device receives powerabove the sensitivity of its energy harvesting system. Therefore, the harvesting48coverage probability is given byPrcoverage = P(PRx > Psensin ) = Q(Psensin −µdBmσdB), (4.10)where Psensin denotes the input sensitivity of the RF energy harvesting system at theAmbient IoT device and Q is the Gaussian-Q function. Although coverage proba-bility does not account for the specifics of charge accumulation at the IoT device,it remains valuable as it is independent of specific power consumption or energyharvesting models. This generality makes Prcoverage meaningful for understandingthe performance of RF energy harvesting in Ambient IoT devices across diversescenarios.4.3.2 Charging ProbabilityEven if a device receives power exceeding the operational threshold of its energyharvesting circuitry, it is essential to ensure that charge accumulates within theenergy storage element for Ambient IoT technologies to function effectively. Weconsider Prcharge as the probability that the device accumulates enough energy inits storage to support its operation after a fixed charging interval.To this end, we consider EON as the energy that is required for a device to beactive or ON. As illustrated in Figure 2.7, this energy corresponds to the ON timethat must be supported by the harvesting or charging interval. Therefore, we denotethe probability that an Ambient IoT device is able to harvest the required energyEON during a harvesting interval asPrcharge = P(Eharv ≥ EON) . (4.11)The harvested energy during a fixed harvesting interval with maximum time T maxchargeis defined asEharv =T maxcharge∫0Estored(t)dt, (4.12)where Estored(t) is defined in Equation 2.23 as the stored energy at time t. Thecharging probability facilitates the comparison of various energy harvesting sources49Parameter ValueCapacitor (C) 100 µFMaximum voltage (Vmax) 1.8VMinimum voltage (Vmin) 1.5VMinimum energy (EON) 112.5 µJReceiving antenna gain 0 dBiOperating frequency 900 MHzLeakage constant (k) 0.01ESR 0.1 ΩMaximum charging time (T maxcharge) 10 minutesTable 4.4: Simulation parameters for a general Ambient IoT device with RFenergy harvesting capabilities.by assessing their effectiveness in charging the energy storage of an Ambient IoTdevice over a defined time interval.4.4 Numerical ResultsIn this section, we utilize the sigmoid energy harvesting model from Chapter 2along with the performance metrics defined previously to analyze the efficacy ofpowering an Ambient IoT device with ambient RF energy.4.4.1 Device ModelDue to the absence of universally defined parameters for Ambient IoT, we start bymodeling the device in question and make the following assumptions to evaluatesystem performance. We assume an Ambient IoT device equipped with a 100 µFstorage capacitor that has a minimum required voltage of 1.5 V, enabling it to storea maximum energy of 112.5 µJ. According to estimates from3GPP, the power con-sumption of active devices in Ambient IoT applications is projected to range from1 µW to 1 mW [3]. For this evaluation, we assume a maximum voltage of 1.8 V,a leakage constant of 0.01, and an equivalent series resistance (ESR) of 0.1 Ω, asdetailed in the studies [40, 41]. The complete set of simulation parameters for theAmbient IoT device referenced in the numerical results is provided in Table 4.4.50Parameter ValueHarvester bandwidth (W ) 35 MHzPsat 0 dBmPsens -30 dBma 42.3b 5.2ηDC-DC 0.8Time step (τ) 1 secondTable 4.5: Parameters for the sigmoid RF energy harvesting model in Ta-ble 2.1 and DC-DC PCE of the PMU.4.4.2 Energy Harvesting ModelBuilding on the discussion presented in Chapter 2, we model the harvested powerusing the sigmoid model outlined earlier. The parameters a and b for this model,along with the values of Psensin , Psatin , and the harvesting bandwidth, are based ondata derived from fitting the data provided in [7]. These values are displayed inTable 4.5, along with other parameters used in the numerical results. We considera fixed DC-DC efficiency of 0.8, as stated in [44], and assume that the ambientpower samples are generated once every second.4.4.3 Coverage AnalysisTo compare the energy harvesting coverage from the measurement locations inTable 4.3, we must first equate the signal bandwidth for each measurement to theeffective bandwidth of the RF energy harvesting system at the Ambient IoT device.We start by adjusting the mean using a scaling factor that accounts for differencesin the RBW between measurements. The scaling factor in dB is calculated asScaling Factor (dB) = 10log10(WRBW), (4.13)where W is the bandwidth of the energy harvester from Table 4.4, and RBW is fromTable 4.2. Using the above, the scaling factors for Canada, Turkey, and Italy areapproximately 8.45 dB, 2.43 dB, and -3.59 dB, respectively. The energy harvestingcoverage for a general Ambient IoT device at each location can be calculated using51Location PrcoverageCanada [62]Urban 0.16Rural 0.04Turkey [64]Indoor - A 0.00Indoor - B 0.00Indoor - E 0.59Semi-indoor - A 0.22Semi-indoor - B 0.98Semi-indoor - E 1.00Outdoor - A 1.00Outdoor - B 1.00Outdoor - C 1.00Outdoor - D 1.00Outdoor - E 1.00Outdoor - F 1.00Outdoor - G 0.00Outdoor - H 1.00Italy [65]Urban 0.82Semi-urban 0.23Rural 0.00Table 4.6: Probability of energy harvesting coverage for ambient RF powermeasurement values, adjusted for effective bandwidth, using Equa-tion 4.10.the adjusted mean and standard deviation input into Equation 4.10. The energyharvesting coverage results for the locations considered are shown in Table 4.6.The results indicate significant variations in coverage across the different mea-surement locations. In Canada, we observe extremely low coverage in both urbanand rural areas, which aligns with the low mean ambient power measurements inthis region. In Turkey, coverage is primarily achievable outdoors, however we dosee some indication of coverage in all semi-indoor locations. The three indoorlocations in Turkey experience no energy harvesting coverage. This discrepancybetween indoor and outdoor coverage is likely attributed to the enhanced infrastruc-ture for cellular networks outdoors compared to indoors [64]. In Italy, coverage ishigher in the urban location, while the rural location shows none.4.4.4 Charging ProbabilityEnergy harvesting coverage provides only part of the picture; it is insufficient toevaluate the success of an Ambient IoT device if it cannot effectively store the har-52Scenario Correlation (p)Low correlation 0.1Medium correlation 0.5High correlation 0.9Table 4.7: Correlation coefficients considered for the numerical results ingenerating pseudo-ambient data for analyzing ambient RF energysources.vested energy. In analyzing the charging of an Ambient IoT device, we considerthree scenarios: low, medium, and high correlation. We expect higher correlationbetween received power values in rural areas, where variations in network activ-ity are minimal. Conversely, lower correlation is anticipated in high-traffic areas,characterized by greater user fluctuations and dynamic environments. To repre-sent these scenarios, we have selected three correlation coefficients, as detailed inTable 4.7.In our analysis, we assume a measurement interval of 1 second and limit oursimulations to a maximum charging time (T maxcharge) of 10 minutes, in line with the de-sign objectives outlined for Ambient IoT in Chapter 3. This results in a total of 600pseudo-ambient power values generated using the AR(1) process. Additionally, weconduct 1000 iterations of data generation for each location under consideration,employing a unique random initialization for each iteration. The mean chargingprobabilities across all iterations are summarized in Table 4.8.These results further illustrate that coverage probabilities alone do not fullycapture the differences in charging performance for Ambient IoT devices. Weobserve that a device can charge only in areas with high coverage, such as mostoutdoor and semi-outdoor locations in Turkey and the urban location in Italy. Thecharging probabilities shown in Table 4.8 are nearly deterministic by location, sug-gesting that the ambient power statistics at a given location, particularly the mean,play a more significant role in charging performance than the selected correlationcoefficient. In all cases where the charging probability is 100% across the threecorrelation values, the bandwidth-adjusted mean (µdBm) exceeds the harvestingsensitivity of -30 dBm.In the semi-indoor A location in Turkey, the adjusted mean falls slightly below53Location PrchargeLow Medium HighCanada [62]Urban 0.00 0.00 0.00Rural 0.00 0.00 0.00Turkey [64]Indoor - A 0.00 0.00 0.00Indoor - B 0.00 0.00 0.00Indoor - E 0.00 0.00 0.00Semi-indoor - A 0.00 0.00 0.00Semi-indoor - B 1.00 1.00 1.00Semi-indoor - E 1.00 1.00 1.00Outdoor - A 1.00 1.00 1.00Outdoor - B 1.00 1.00 1.00Outdoor - C 1.00 1.00 1.00Outdoor - D 1.00 1.00 1.00Outdoor - E 1.00 1.00 1.00Outdoor - F 1.00 1.00 1.00Outdoor - G 0.00 0.00 0.00Outdoor - H 1.00 1.00 1.00Italy [65]Urban 1.00 1.00 1.00Semi-urban 0.00 0.00 0.00Rural 0.00 0.00 0.00Table 4.8: Charging probability of an Ambient IoT device at the ambient RFmeasurement locations in Table 4.3.the sensitivity threshold at -32.01 dBm. Although the generated power data underthe high-correlation model occasionally exceeds the sensitivity, such occurrencesare too infrequent to charge the capacitor within T maxcharge. In Figure 4.3, we illustratethe storage capacitor’s voltage level for the medium correlation case at this loca-tion. Despite achieving a coverage probability of Prcoverage = 0.22, the low meanpower reduces the time spent above the harvesting sensitivity, preventing chargeaccumulation beyond 0.3 V. Under the high correlation model, the capacitor volt-age observed for this location stabilizes at approximately 0.9 V.54Figure 4.3: Example of the storage capacitor voltage for Turkey’s semi-indoor location A under medium correlation conditions. While chargebegins to accumulate in the storage capacitor, the voltage levels offaround 0.3 V, ultimately resulting in very low charging probabilities forthis scenario.The charging probabilities for the RF sources considered in this chapter in-dicate that designing an Ambient IoT system capable of reliably charging solelyfrom ambient RF signals across all locations — outdoors, indoors, urban, and rural— will be very challenging. While the measurement results from outdoor loca-tions in Turkey suggest a high probability of charging (nearly 100% for all sites),indoor locations experience complete charging outages. Consequently, a differentapproach must be considered to ensure the sustainable operation of Ambient IoTdevices indoors.55Chapter 5Harvesting from Dedicated RFSourcesPowering Ambient IoT devices necessitates the use of predictable and reliable en-ergy sources to ensure effective charging. In Chapter 4, we discussed the inher-ent variability and low average power levels of ambient RF signals, particularlyin indoor environments. Consequently, this chapter shifts its focus to dedicatedRF energy sources for powering Ambient IoT devices. Specifically, we use theterm power beacon (PB) to refer to infrastructures designed for RF energy har-vesting that provides a controlled source of energy. Unlike ambient sources, PBsoffer consistent and predictable energy availability, making them especially advan-tageous for battery-less Ambient IoT devices.This chapter explores the deployment of PBs, starting with the application ofstochastic geometry to model PB placement and assess various strategies for inte-grating PBs into existing network infrastructures. We then investigate how multi-antenna PBs can enhance directivity and increase the received power at AmbientIoT devices without requiring CSI at the PB. A system model is presented to eval-uate this approach. Finally, numerical results are analyzed, focusing on harvestingcoverage and charging probability, and these findings are compared with those inChapter 4.565.1 Deployment of PBsDeploying a network of PBs can provide broader coverage and ensure consistentenergy harvesting, unlike approaches such as [27], which focus on energy from asingle dedicated source. We conceptualize PBs as fixed infrastructure with knownlocations, in contrast to the often uncertain and potentially mobile positions of Am-bient IoT devices. Optimizing the spatial placement of PBs allows for greater flex-ibility in reducing dead zones and minimizing propagation losses, as the receivedpower decays exponentially with distance from the PB, as shown in Equation 2.7.Rosabal et al. demonstrated that uniform spacing of single-antenna PBs is idealwhen environmental factors, such as obstacles, are unknown, as most energy isharvested from the nearest PB [70]. However, deploying PBs with equal spac-ing necessitates new infrastructure, as real-world network nodes are rarely evenlydistributed.A more cost-effective alternative could involve repurposing existing networkinfrastructure. For instance, in indoor environments, cellular pico-cell BSs or Wi-Fi APs could be adapted to function as PBs. In these scenarios, the spatial layoutcan be modeled using stochastic geometry and random point processes, incorpo-rating design-specific parameters that accurately reflect real-world deployments.5.1.1 Stochastic GeometryStochastic geometry is a mathematical framework that utilizes probabilistic andanalytical methods to analyze the spatial distribution of points within a given area.This methodology allows designers to assess wireless networks over multiple spa-tial realizations, incorporating elements that closely reflect real-world conditions [71].A point process, denoted as Φ, can be used to represent data localized in space.In this case, the realized point process Φ = {x1,x2, . . .}, where each xi, i ∈ N,represents the location of a point, or a PB in the context of this work. A Poissonpoint process (PPP) is a commonly used model in wireless networks, characterizedby an intensity λP, and it possesses two key properties, as described in [72]:1. The number of points in any set B ⊂ R2 is a Poisson random variable with57mean λP|B|,P(Φ(B) = k) = e−λP|B|(λP|B|)2k!. (5.1)2. The number of points in disjoint sets are independent.The PPP is useful for modeling random locations of network nodes, but real-worlddeployments typically involve some level of structure [73]. An example where thePPP may be appropriate is in modeling rural cellular BSs, which are characterizedby low density and high randomness. However, in scenarios where a fixed mini-mum distance between sources is required, such as in high density deployments ofWi-Fi APs, the Mate´rn hard-core process (MHCP) is more suitable. This processis derived by thinning a PPP, and the steps, as outlined in [73], are as follows:1. Generate a PPP ΦP with density λP.2. For each point x ∈ ΦP, assign a random “birthday” bx i.i.d.∼ U [0,1]. This no-tation indicates that each birthday is assigned as a uniformly distributed ran-dom value, independently sampled from the others.3. Remove any point x if it lies within a distance r (inside the ball B(y,r)) ofany older point y based on their assigned birthdays. That is, y is older than xif by < bx. The resulting point process has a new intensity λM.The process described above is referred to as a hard-core process, due to its strictrule that removes all points within a specified distance of other points. In practice,this can be useful to incorporate a minimum separation distance required betweennetwork nodes, as shown in Appendix A for 3GPP BSs and user equipment (UE).Clearly, the thinning that takes place with the MHCP above results in λM ≤ λP.Following [74], the resulting intensity of the MHCP, λM, is given byλM =1− e−λPπr2πr2. (5.2)If it is desired to compare a PPP and an MHCP with the same resulting intensity,we can adjust the starting intensity of the MHCP in step 1 above to beλ′P =−1πr2ln(1−πr2λM), (5.3)58(a) Poisson point process. (b) Grid of evenly spaced elements.(c) Evenly spaced elements with anoffset.(d) Matern hard-core process with re-pulsion distance r = 0.05.Figure 5.1: Examples of point processes, each normalized to unit dimensions.All processes are simulated with an intensity of λ = 0.0025.where λM is the desired intensity of the resulting realization.5.1.2 Practical ImplementationsTraditionally, cellular BSs were modeled as following a hexagonal lattice struc-ture. However, in reality, BSs exhibit significant randomness and rarely conformto deterministic patterns. For instance, cellular macro-cell BSs have higher powertransmissions and greater separation distances, which often results in a more grid-like distribution. In contrast, mobile devices such as UEs and IoT devices haveinherently random locations and are typically represented using a PPP [73]. Toaccurately model the placement of PBs, it is crucial to incorporate both design pa-59Point Process ApplicabilityEven spacing (grid) Indoor Wi-Fi APs, cellular Femto-cell BSsEven spacing (off-set) Indoor Wi-Fi APs, cellular Femto-cell BSsPoisson Rural cellular Macro-cell BSs, IoT Devices, UEMate´rn hard-core Urban cellular BSs, Wi-Fi APsTable 5.1: Stochastic geometry point processes and their relevance to currentwireless networking infrastructure.rameters and elements of randomness to reflect real-world conditions. While we donot consider one specific type of communication infrastructure as a PB, differentnetwork infrastructure may fit into multiple categories depending on their deploy-ment context. A non-exhaustive list of examples is provided in Table 5.1, and anillustration of the four processes is shown in Figure 5.1.5.2 Multi-antenna PBsThe dispersive nature of RF energy when used in an underacted manner stronglysuggests the incorporation of directivity at PBs. Directivity techniques, such asenergy beamforming (EB) [75], can enhance the received power for energy har-vesting devices by making used of multi-antenna PBs. Although increasing thenumber of PBs has a greater impact on coverage than increasing the number oftransmitting antennas [70], directing power through beamforming can reduce theneed for a high-density PB deployment, thereby lowering infrastructure costs. Byconcentrating energy into narrower beams, multi-antenna PBs extend the range atwhich energy can be harvested. Research into the antenna configurations for PBshas primarily focused on half-wavelength uniform linear arrays (ULAs), owingto their mathematical tractability and consistent gain [27]. For instance, consid-ering free-space path loss (Equation 2.6), a single 23 dBm transmitter utilizing a4-element ULA can extend the effective energy harvesting range to approximately23.7 meters, compared to only 11.8 meters achievable by a single omnidirectionalantenna, assuming a sensitivity of -30 dBm with a 0 dBi receiving antenna.60Figure 5.2: Structure of a 4 element ULA transmitter, with θ representingthe steering angle relative to the boresight shown in blue. The distancebetween subsequent antenna elements is d, which we assume to be equalto λ/2 for the case of half-wavelength spacing.5.2.1 Uniform Linear ArrayA ULA consists of uniformly spaced elements arranged linearly, with half-wavelengthspacing being a standard choice. An illustrative example of a ULA is provided inFigure 5.2.A ULA is classified as broadside when the main beam is oriented perpendicu-lar to the array’s axis at boresight (i.e., θ = 0◦). Conversely, an end-fire array hasits main beam aligned with the axis of orientation (i.e., θ = −90◦,90◦). For thespecific configuration of a half-wavelength spaced ULA, utilizing M isotropic ele-ments, a stable gain of M is achieved when steering the beam across various anglesfrom the boresight [76]. In Figure 5.3, we show the gain pattern for a 4-elementhalf-wavelength ULA transmitting along the boresight and steering the main beam90◦ toward the end-fire direction.5.2.2 CSI-free Energy BeamformingMulti-antenna directivity techniques generally require precise CSI for optimal sig-nal transmission. However, the power and complexity constraints inherent in Am-bient IoT devices restrict the overhead associated with acquiring CSI at a PB.61(a) Boresight for M = 4. (b) End-fire for M = 4.Figure 5.3: Gain patterns for a 4-element half-wavelength spaced ULA ar-ranged along the y-axis in the azimuth plane. These plots displays avertical cut of the 3D radiation pattern at zero elevation.Consequently, adopting CSI-free directivity schemes is advisable as a baseline ap-proach. These techniques, such as those highlighted in [27], enable PBs to directenergy without depending on channel statistics or specific device location knowl-edge.Several CSI-free multi-antenna strategies have been introduced in previousstudies [17, 77–79]. However, these analyses often overlook the implications ofstoring harvested energy for future use, focusing primarily on the gains associatedwith simplified PCE models for energy harvesting devices. To address this gap,our investigation explores the advantages of implementing multi-antenna, CSI-freeschemes in deployments involving multiple PBs while assessing both energy har-vesting coverage and charging reliability for Ambient IoT devices.We examine three CSI-free techniques alongside the scenario of single-antennaPBs. An overview of these methods is provided below and depicted in Figure 5.4.While these techniques serve as key representatives of transmission strategies forEB, they do not encompass the entirety of potential approaches; a more extensivesurvey of methods is available in [80]. The CSI-free schemes considered as asfollows:• One-antenna (OA): The PB transmits omnidirectionally at the rated trans-mission power using a single antenna.62(a) Transmission with a singleisotropic antenna (OA), resulting in anomnidirectional radiation pattern. De-vices outside this constant coveragearea will not experience any powertransfer.(b) Multi-antenna PBs transmit-ting with EB, which could be eithersequential SEB or REB. Devices mayexperience intermittent coverage dueto the dynamic radiation pattern fromthe scanning power beam.(c) Multi-antenna PBs equippedwith hardware for MR. The radiationpattern remains fixed in the end-fireorientation, but the PB’s physical di-rection changes over time.Figure 5.4: Energy harvesting coverage for various CSI-free transmissionschemes considered in this work. The green and orange coverage ar-eas illustrate the radiation patterns for different beam steering anglesand mechanical rotations in SEB, REB, and MR configurations.63• Energy beamforming (EB): The PB uses beamforming techniques to trans-mit from all antennas simultaneously, sequentially changing the direction ofmaximum gain. We refer to this technique as sequential energy beamform-ing (SEB). Another variation of EB is random energy beamforming (REB),where the direction of the main lobe is changed randomly during the trans-mission interval.• Mechanical rotation (MR): The PB is equipped with a mechanical motorthat allows it to be physically rotated during transmissions. Unlike SEB andREB, in MR the beam pattern remains in a constant, end-fire orientationthroughout all transmissions [17].5.3 System ModelTo evaluate RF energy harvesting from dedicated sources, we consider a set of Ndeployed PBs serving a single Ambient IoT device. The locations of the PBs aremodeled on the Euclidean plane as a point process ΦPB with a spatial density ofλPB PBs/m2.Similar to the assumptions made in Chapter 4 regarding time-varying power,we assume that the channels between the PBs and the Ambient IoT device arequasi-static, meaning the received power at the device does not vary over a singletime step. In the following, we denote the equivalent complex baseband channelbetween the j-th PB and the Ambient IoT device as h j ∈CM×1 and combined pathloss and shadowing represented by β j, as defined in Equation 2.10. Each PB isequipped with M antennas, where M = 1 represents the OA case.5.3.1 Signal ModelThe power transmitted by each PB can be modeled as the transmission of pseudo-data for the purpose of transferring power rather than information, a concept de-veloped in SWIPT [16]. We denote the energy signal from the j-th PB as x j andconsider it to be independent of signals transmitted from other PBs. Additionally,we assume that it is power normalized for a given transmit power PTx, j. Therefore,64we get the following:E[|x j|2]= PTx, j (5.4)E[x∗jxi]= 0, ∀ j ̸= i. (5.5)Additionally, we assume that all PBs transmit at the same rated power, given byPTx, j = PTx, ∀ j = 1, . . . ,N.To model the effective gains achieved through the use of multi-antenna PBs, weemploy associated precoding vectors w j ∈CM×1, which will be detailed in the fol-lowing section. Therefore, there signal received at the Ambient IoT device trans-mitted from the j-th PB is given byy j =√β jhTj w jx j, j = 1, . . . ,N. (5.6)The received average power at the Ambient IoT device, assuming a 0 dBi receivingantenna, as a result of the power transmitted from the j-th PB isPRx, j = Ex[|y j|2]= Ex[(√β jhTj w jx j)H(√β jhTj w jx j)]= β j|hTj w j|2Ex[|x j|2]= β jPTx|hTj w j|2.(5.7)The total average power received at the Ambient IoT device considering all de-ployed PBs can be written asPRx = ∑ΦPBPRx, j= ∑ΦPBβ jPTx, j|hTj w j|2= |ΦPB|PTxN∑j=1β j|hTj w j|2,(5.8)where |ΦPB| is the number of PBs realized in the process ΦPB.655.3.2 Channel ModelThe deployment environment of Ambient IoT devices and PBs greatly influencesthe channel between them, which is typically classified as either LOS or non-line-of-sight (NLOS). LOS channels offer direct signal paths, resulting in strongerconnections, while NLOS channels encounter obstructions, leading to signal weak-ening and multi-path effects. In this work, due to the uncertainty of deploymentenvironments, we account for both LOS and NLOS scenarios. For simplicity, weassume a unified channel model for both cases, but we penalize NLOS conditionsby incorporating additional path loss and shadow fading. This approach allows usto focus on attenuation rather than the complexities of multi-path effects, which inreality are more significant in NLOS scenarios. By modeling path loss and shad-owing, we can effectively capture the distance dependence and the impact of largerobstacles, which are key factors in determining RF energy availability over largerareas.The m-th component of the deterministic, or LOS, channel h j between andAmbient IoT device and the j-th PB can be written ash j,m = e−j(m−1)2πdλ sin(θ j), (5.9)where θ j ∈ [−π,π] s the azimuth angle of the Ambient IoT device relative to theboresight of the j-th PB’s antenna array.To simulate path loss and shadowing, we utilize 3GPP 5G channel models.For each PB, a random value C ji.i.d.∼ U [0,1] is sampled independently to determinethe LOS probability of the associated channel, following the procedure outlined in[4]. If C j ≤ PrLOS, j, the PB is assigned a LOS channel; otherwise, it is assignedan NLOS channel. We then apply the respective path loss model (either LOS orNLOS) to calculate the path loss L jdB for each PB. As in Chapter 4 for ambientRF sources, shadow fading is assumed to follow a log-normal distribution, withstandard deviations given in Tables B.1 and B.2 for InH and InF environments,respectively. The combined path loss and shadow fading term for the j-th PB,shown in Equation 5.7, is expressed asβ j = 10β jdB10 , (5.10)66where β jdB is computed following Equation 2.10.5.3.3 CSI-Free Multi-antenna SchemesIn multiple-input single-output (MISO) systems, like those involving multi-antennaPBs, precoding vectors are employed to optimize signal transmission from multi-ple antennas. A precoding vector w ∈ CM×1 defines the relative signal transmittedfrom each antenna element of a PB. In general, the m-th element of the precodingvector for the j-th PB can be computed asw j,m =√1Me−j(m−1)2πdλ sin(θsteer, j)=√1Me−j(m−1)π sin(θsteer, j)(5.11)Here, the second line arises from using half-wavelength spaced elements, andθsteer, j represents the angle at which the PB steers its main beam. The factor√1M ensures that PTx stays consistent for all values of M. We can use the termG(θ j,w(θsteer, j)) to describe the gain experienced, over a single isotropic antenna,by the Ambient IoT device which is angle θ j from the PB employing the precodingscheme w and steering in θsteer, j. It is calculated asG(θ j,w(θsteer, j)) =PRx, j(θ j,w(θsteer, j))PTxβ j, (5.12)where PRx, j(θ j,w(θsteer, j)) can be calculated using Equation 5.7 for each PB. Theprecoding vectors for the four CSI-free schemes introduced in Section 5.2.1 arediscussed below.One-antenna (OA)For the case of a single transmitting antenna at each PB (i.e., M = 1), the precodingscaler is given bywOA = 1.The above is under the assumption of the single antenna being isotropic. Thisscheme can be referenced as a baseline between the other multi-antenna tech-67niques.Sequential Energy Beamforming (SEB)SEB is inspired by beam scanning techniques commonly used in mmWave sys-tems. In beam scanning, the transmitted signal is electronically steered in variousdirections to maintain strong, focused connections at higher frequencies, wherepower attenuation is more significant [81]. In this work, we consider that the steer-ing angle in SEB is sequentially adjusted to cover all directions. Therefore, thesteering angle becomes a function of time and is given byθSEB(t) = θ0+(t−1) ·θstep, t = 1, . . .T. (5.13)In the above, θ0 represents the starting orientation of a PB, θstep represents the sizeof shift that is taken at each time step t, and T is the total number of time steps thatwe consider. The complete precoding vector for SEB can be determined bywSEB(t) =√1M[1,e−j sin(θSEB, j(t)), . . . ,e−j(M−1)π sin(θSEB, j(t))]T. (5.14)When choosing the value of θstep for SEB, we must consider the directivity of theULA, which provides advantages over the OA case. For a half-wavelength spacedULA with M elements, the gain of the main beam is M, while the half-power gainis M/2. To ensure that Ambient IoT devices remain within at least the half-powergain, we must select an appropriate step size. In beam scanning, the radiationpattern changes as the beam is steered. The half-power beam width is minimizedin the broadside direction [76, 82], and can be calculated for an M element array as∆θ3 dBmin ≈0.886cos(θsteer)λMd=1.772M,(5.15)The second line comes from the assumption of half-wavelength spacing. For thecase of M = 4, we get that ∆θ3 dBmin = 0.443 rad, or approximately 25.4◦. There-fore, for an Ambient IoT device to always receive at least half of the maximum di-68rectivity within a full 360-degree scanning rotation, we must select θstep ≤∆θ3 dBminand ensure that the PB scans over the entire range from 0 to 2π radians.Random Energy Beamforming (REB)Compared to SEB, with REB we introduce the effect of randomly varying thedirection between a set of predefined increment steps. For consistency, we usethe same set of steering directions as in SEB, resulting in the following possiblesteering directions given byθREB, j ∈{nθstep | n ∈ Z,0≤ nθstep ≤ 2π}. (5.16)At each time step t, we assume that θREB, j(t) is randomly selected from this set ofangles. In contrast to SEB, REB does not guarantee that every Ambient IoT de-vice will align with the half-power gain direction. However, the random variationin angles between PBs and devices can enhance network diversity, occasionallysurpassing the gains made through of SEB and potentially improving overall per-formance.Mechanical Rotation (MR)A concern with SEB and REB is that the shape of the main lobe changes throughoutthe beam scanning process. Following [76], the half-power beamwidth is maxi-mized for a half-wavelength ULA oriented in end-fire. Knowing this, MR takesinspiration from transmitting always in the end-fire orientation, and physically ro-tating the PB. Therefore, the resulting radiation pattern stays consistent in shape.This is accomplished by alternating the phase the signal transmitted at subsequentantenna elements [17]. Therefore, the associated precoding vector is given bywMR =√1M[1,−1, . . . ,1]T . (5.17)For the case of MR, it is the channel vector that changes while the PB is steered.That is, that the the angle between the Ambient IoT device and the j-th PB em-69ploying MR at time t is given byθ j(t) = θ j0−θMR, j(t), θMR, j(t) ∈ [0,2π), (5.18)where θMR, j is the angle that the j-th PB has been rotated. It was demonstratedin [17] that the minimum number of steps required for an Ambient IoT device tobe guaranteed within the half-power beamwidth is M− 1, although MR can beimplemented with additional steps.5.4 Numerical ResultsIn this section, we analyze the performance of dedicated RF sources for poweringAmbient IoT devices. Given that the results presented in Chapter 4 revealed limitedcoverage from ambient RF sources indoors, our simulations will specifically targetthese environments.5.4.1 Deployment ScenariosTo evaluate the performance of charging Ambient IoT devices using dedicated RFsources, we consider three deployment scenarios, with simulation parameters out-lined in Table 5.2. The simulations involve placing PBs within a 100m× 100marea. The locations are modeled using the point process, ΦPB, and the initial ori-entations of the PBs are randomized within the interval from 0 to 2π . All device-specific parameters and the energy harvesting model, as outlined in Tables 4.4 and4.5, are taken into account.For multi-antenna PBs, while increasing the number of antenna elements canenhance received power, it may also narrow the beamwidth, leading to missedenergy harvesting opportunities. Since we assume each ULA consists of half-wavelength spaced elements, an increase in M will also expand the physical size ofthe antenna array at each PB. In our simulations, we use a value of M = 4, whichstrikes a balance by achieving a maximum gain of 6.02 dBi while minimizing theoverall size of the antenna array.In Scenario 1, PBs transmit at power levels comparable to those of cellularUEs. We employ the 3GPP InH path loss model and model the locations of the70Parameter ValueDimensions 100 m × 100 mθstep-size π12 radiansTime step (τ) 10 secondsPB ULA elements (M) 4PB ULA spacing λ2Height PB (hPB) 5 mHeight Ambient IoT (hA-IoT) 1.5 mScenario 1Transmit power (PTx) 24 dBmPoint process Poisson3GPP channel model InHScenario 2Transmit power (PTx) 30 dBmPoint process Matern Hard-coreRepulsion 3 m3GPP channel model InHScenario 3Transmit power (PTx) 30 dBmPoint process Even spacing (off-set)3GPP channel model InF-DHTable 5.2: Simulation parameters for deploying dedicated PBs to power Am-bient IoT devices. Three scenarios are considered.PBs using a PPP. Scenario 2 simulates a deployment typical of Wi-Fi APs or pico-cell BS, with a transmission power of 30 dBm. In this case, we introduce a fixedrepulsion of 3 m between elements to reflect standard IEEE AP deployments [83].As in Scenario 1, we again use the 3GPP InH path loss model. Finally, Scenario 3explores the deployment of new infrastructure designed for Ambient IoT, featur-ing PBs arranged in an evenly spaced offset formation. This scenario utilizes the3GPP InF dense clutter (InF-DH) path loss model, which has been discussed interms of its applicability for Ambient IoT applications, primarily within industrialenvironments [60].5.4.2 Coverage AnalysisSimilar to Chapter 4, we begin by examining the energy harvesting coverage forAmbient IoT devices based on deployments of PBs. We simulate each of the sce-71PB Density (per 100m2) Technique PrcoverageScenario 1 Scenario 2 Scenario 30.05OA 0.20 0.86 0.86SEB 0.49 0.99 0.92REB 0.56 1.00 0.99MR 0.57 1.00 0.990.1OA 0.46 0.99 0.99SEB 0.81 1.00 1.00REB 0.87 1.00 1.00MR 0.88 1.00 1.000.2OA 0.89 1.00 1.00SEB 0.99 1.00 1.00REB 0.99 1.00 1.00MR 1.00 1.00 1.000.3OA 0.99 1.00 1.00SEB 1.00 1.00 1.00REB 1.00 1.00 1.00MR 1.00 1.00 1.000.4OA 1.00 1.00 1.00SEB 1.00 1.00 1.00REB 1.00 1.00 1.00MR 1.00 1.00 1.000.5OA 1.00 1.00 1.00SEB 1.00 1.00 1.00REB 1.00 1.00 1.00MR 1.00 1.00 1.00Table 5.3: Energy harvesting coverage resulting from deployments of PBs.Coverage is evaluated for four CSI-free transmission schemes across var-ious deployment densities of PBs.narios with densities ranging from 0.05 PBs/100 m2 to 0.5 PBs/100 m2 for the fourCSI-free transmission schemes. To evaluate coverage, we calculate the probability,over 1000 randomly initialized iterations, that the received power remains belowthe system sensitivity threshold of -30 dBm. The coverage results are presented inTable 5.3.The results indicate that energy harvesting coverage is strongly influenced bythe transmission power of the PBs. This is evident in Scenario 1, where PBs trans-mitting at 23 dBm required roughly three times the density seen in Scenario 2 toachieve near complete coverage. Additionally, we observe that Scenario 2 andScenario 3 exhibit similar coverage values across all densities.In all scenarios, the coverage provided by the REB and MR schemes is quite72PB Density (per 100m2) Technique PrchargeScenario 1 Scenario 2 Scenario 30.05OA 0.11 0.63 0.61SEB 0.06 0.50 0.45REB 0.06 0.55 0.55MR 0.12 0.79 0.920.1OA 0.30 0.96 0.99SEB 0.18 0.97 1.00REB 0.22 0.98 1.00MR 0.38 1.00 1.000.2OA 0.69 1.00 1.00SEB 0.59 1.00 1.00REB 0.67 1.00 1.00MR 0.90 1.00 1.000.3OA 0.92 1.00 1.00SEB 0.88 1.00 1.00REB 0.93 1.00 1.00MR 0.99 1.00 1.000.4OA 0.98 1.00 1.00SEB 0.99 1.00 1.00REB 1.00 1.00 1.00MR 1.00 1.00 1.000.5OA 0.99 1.00 1.00SEB 1.00 1.00 1.00REB 1.00 1.00 1.00MR 1.00 1.00 1.00Table 5.4: Charging probabilities for Ambient IoT devices resulting from de-ployments of PBs. The charging performance is evaluated for four CSI-free transmission schemes across various PB deployment densities.similar, with MR slightly outperforming REB. The OA scheme, particularly inScenario 1, delivers the lowest coverage. Overall, the energy harvesting coverageresults for dedicated sources significantly surpass those for indoor and semi-indoorambient sources presented in Table 4.6.5.4.3 Charging ProbabilityTo evaluate the impact of deploying dedicated RF sources on the charging perfor-mance of Ambient IoT devices, we adopt the same approach for assessing charg-ing probability as outlined in Section 4.4.4. In Table 5.4, we present the coverageprobabilities for the three scenarios previously introduced, assuming a maximumcharging time, T maxcharge, of 10 minutes.73The results in Table 5.4 support previous findings on energy harvesting cover-age, indicating that transmitting at higher power enhances charging for the samePB density. In low-density PB deployments, the OA technique outperforms boththe SEB and REB approaches, even rivaling MR in Scenario 1. The consistent,omnidirectional radiation pattern achieved through OA effectively reduces cover-age gaps across the deployment area. In contrast, SEB and REB experience largergaps due to their inconsistent and narrower beam scanning patterns, particularlynoticeable at lower PB densities.Across all scenarios, MR demonstrates superior performance, achieving bothhigher coverage and charging probabilities. This can be attributed to MR’s end-fireradiation pattern, which allows for greater gain over a broader beamwidth, facil-itating faster capacitor charging, especially at elevated input powers. In contrast,SEB and REB show no meaningful advantage over MR in CSI-free scenarios andgenerally under perform. Modifying the REB scheme to include a wider range ofdirectivity angles, rather than maintaining a fixed step size based on SEB, could en-hance coverage by increasing diversity and capturing missed Ambient IoT deviceswhen scanning.From these results, we can confirm that charging Ambient IoT devices throughRF energy harvesting is more viable in environments with densely deployed PBs.However, Scenario 1 highlights challenges in harvesting energy from lower-powerdedicated sources, such as UEs, suggesting that if infrastructure reuse is prioritized,PBs should emulate higher-power sources. Although MR outperforms SEB andREB, incorporating motorized rotation into existing infrastructure could be costlyand impractical, making SEB and REB more feasible alternatives that still offerconsiderable charging gains over OA.Overall, the results for CSI-free multi-antenna schemes at higher PB densities,particularly in Scenarios 2 and 3, exhibit charging probabilities similar to thoseobserved in fully ambient outdoor cases explored in Chapter 4. Therefore, therewould not be a large benefit to placing dedicated PBs outdoors. In contrast, thecharging probabilities for the dedicated case shown in Table 5.4 are less deter-ministic than those in the ambient cases presented in Table 4.8. This discrepancyarises from the vastly different received power levels across different realizations,especially in Scenarios 1 and 2, where PB deployments under the PPP and MHCP74exhibit significant randomness. Our assumptions in this chapter posit that all PBstransmit simultaneously, which may not be feasible, particularly concerning in-frastructure reuse. Moreover, our analysis assumes static devices that are unable toharvest energy during outages for a given PB configuration; however, mobile Am-bient IoT devices would likely encounter temporary outages alongside recurringcharging opportunities.In our analysis of the indoor integration of dedicated PBs, we focused on thepotential for energy harvesting from these controllable sources, omitting the in-fluence of additional ambient RF sources that would coexist in real-world envi-ronments. However, it is crucial to acknowledge that both dedicated and ambientsources will be present upon deployment. As demonstrated in Chapter 4, ambi-ent RF sources indoors exhibit bandwidth-adjusted mean values that fall below thesensitivity threshold for energy harvesting. Consequently, their contribution to thetotal received power during charging periods is minimal when considered along-side dedicated, specifically multi-antenna, PBs.Regarding the practical deployment of Ambient IoT devices, our findings fromChapters 4 and 5 suggest that a hybrid approach could be effective. In this ap-proach, Ambient IoT devices would primarily utilize ambient energy sources foroutdoor deployments while relying on dedicated, higher-power PBs indoors. Fur-thermore, if infrastructure reuse is implemented for PBs in a time-division man-ner, where the physical infrastructure serves an alternative communication purposewhen not transmitting a dedicated signal for power transfer, this downtime couldeffectively be modeled as harvesting from an ambient source, with dedicated en-ergy harvesting occurring during transmission.75Chapter 6Conclusions and Future Work6.1 ConclusionsIn this thesis, we have investigated the reliable and sustainable charging of a newclass of RF energy harvesting IoT devices known as Ambient IoT. We focused onthe fundamentals of RF energy harvesting, comparing existing models and adapt-ing them to improve the performance of Ambient IoT devices. By analyzing howenergy harvesting can be integrated into these devices, we aimed to enhance theiroverall functionality and efficiency. A holistic approach was proposed, consider-ing both the conversion of RF energy to DC power and the lossy process of chargeaccumulation in energy storage elements, which we modeled as capacitors.Chapter 3 provided essential background on Ambient IoT technology and dis-cussed the ongoing standardization efforts within the 3GPP, IEEE, and BluetoothSIG. As Ambient IoT is positioned between existing IoT and adjacent technolo-gies, we examined the characteristics of current systems and identified key differ-ences. We also outlined the design requirements and representative use cases forAmbient IoT, addressing the challenges associated with powering these devices,which set the stage for our investigation into suitable RF energy harvesting sources.In Chapter 4, we focused on leveraging purely ambient RF signals to powerAmbient IoT devices. We reviewed various surveys that measured ambient RFpower levels, noting the variability in the campaigns and suggesting adjustmentsfor better representation. By generating pseudo-ambient data based on the mean76and standard deviation of measured values and assuming a log-normal distributionakin to shadow fading, we applied a first-order autoregressive process for simu-lating ambient data under different correlation models. We introduced two per-formance metrics: energy harvesting coverage, which reflects the probability thatan Ambient IoT device can receive power above the harvesting threshold during acharging interval, and charging probability, which indicates average charge accu-mulation performance. The results revealed that while energy harvesting coverageand charging probabilities are near-zero for indoor environments, values are sig-nificantly higher for certain outdoor scenarios. However, we also found that highfluctuations in received power in areas with considerable variation led to unreliablecharging.In Chapter 5, we shifted our focus to the potential of dedicated power transmit-ters, or PBs, for charging Ambient IoT devices. We discussed modeling PB deploy-ments using stochastic geometry and explored how multi-antenna systems couldenhance received power at devices. By introducing three multi-antenna schemesthat operate without CSI and comparing them to a baseline single-antenna setup,we simulated harvesting coverage and charging probabilities in various scenarios.The scenarios considered were representative of those where PBs could be de-ployed using existing communication infrastructure, such as cellular UEs, indoorBSs, or indoor APs, as well as scenarios where PBs were uniformly deployed solelyfor Ambient IoT energy harvesting. The results indicated that, contrary to the weakindoor coverage from ambient RF signals, denser deployments of PBs combinedwith multi-antenna configurations resulted in high coverage and reliable charging.Notably, mechanical rotation of the multi-antenna PBs produced the best perfor-mance, although practical implementation challenges were acknowledged.Overall, our study demonstrated that dedicated RF sources offer significant ad-vantages over ambient RF signals, providing more consistent and efficient energyharvesting for Ambient IoT devices, particularly in indoor environments. We alsoexplored the potential coexistence of ambient and dedicated sources in real-worldsettings.776.2 Future WorkThis work has illuminated the significant challenges in achieving both coverageand reliable charging for Ambient IoT devices using RF energy harvesting. Ouranalysis of ambient and dedicated RF sources has shown that there is no singlesolution to meet the diverse needs of these devices. The performance of AmbientIoT devices will depend on a variety of factors, such as the power demands of theircommunication protocols, the deployment environment, and the specific energyharvesting design within the device. The process of standardizing Ambient IoT anddetermining device powering will be complex, requiring solutions to be tailored tospecific use cases and operational requirements.Moving forward, extensive measurement campaigns will be critical to gaining aclearer understanding of the ambient RF energy landscape. The results in this workwere based on a few, highly varied ambient RF measurement surveys, but large-scale global measurements will be necessary to evaluate how Ambient IoT devicesperform in different real-world conditions. Furthermore, assumptions about thecorrelation of ambient signals in our simulations should be refined through moreaccurate data on time-varying power levels in real environments.Regarding dedicated RF sources, additional research is necessary to exploreoptimal PB deployment strategies. This includes examining the benefits of usingpartial CSI to improve energy harvesting efficiency and the trade-offs related toAmbient IoT power consumption. Furthermore, the interaction between spectrumusage, regulatory compliance, and safety concerns must be thoroughly evaluated tofacilitate effective power transfer while avoiding interference or health risks beforeimplementation. Up to now, ambient and dedicated sources have been treated asdistinct; however, future studies should investigate the effects of harvesting energyfrom both sources concurrently.78Bibliography[1] R Venkatesha Prasad, Shruti Devasenapathy, Vijay S Rao, and Javad Vazife-hdan. 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Thesis/Dissertation
eng
Electrical and Computer Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Lampe, Lutz
Harnessing RF energy for sustainable power in Ambient IoT devices
Text