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Abstract

The evolution toward sixth generation (6G) wireless networks demands transformative technologies to enable ubiquitous coverage, ultra-reliable communication, and seamless integration of aerial and ground users. A promising candidate for future wireless networks is cell-free massive multiple-input multiple-output (CF-mMIMO), which eliminates cell boundaries by coordinating distributed access points (APs). However, CF-mMIMO faces critical limitations: spectral efficiency can degrade in challenging propagation environments, dense AP deployments increase energy and fronthaul costs, and conventional architectures struggle to support unmanned aerial vehicles (UAVs). This thesis addresses these challenges by incorporating reconfigurable intelligent surfaces (RIS)—a low-cost, energy-efficient technology that dynamically shapes the wireless environment—into CF-mMIMO networks. In the first part, we analyze the downlink performance of multi-RIS-aided CF-mMIMO under realistic conditions, including correlated Rician fading, discrete phase shifts, and imperfect channel state information. We derive closed-form spectral efficiency bounds for conjugate beamforming and zero-forcing precoding, and show that distributed RIS deployment outperforms centralized setups. We also uncover a non-monotonic relationship between RIS quantity and throughput: an optimal number of RISs exists, beyond which interference degrades performance. A genetic algorithm-based phase-shift optimization framework is proposed to maximize throughput. In the second part, we tackle the UAV–ground user coexistence challenge. Down-tilted AP antennas degrade UAV signal quality. We propose a RIS-aided framework that improves UAV connectivity while maintaining ground user quality-of-service. A max-min SINR optimization jointly allocates power and designs RIS phase shifts. Simulations show significant improvement in UAV downlink rates over cellular-connected UAV systems, with no compromise in ground user performance. In the final part, we integrate simultaneously transmitting and reflecting RIS (STAR-RIS) with integrated sensing and communication (ISAC) to support UAVs. The STAR-RIS enables joint transmission to UAVs and reflection to ground users, while using the same waveform for UAV localization. Once localized, the STAR-RIS reconfigures to boost UAV spectral efficiency. This dual-purpose design achieves up to 200% rate improvement and 55% higher sensing accuracy over benchmarks. Overall, this thesis establishes RIS as a key enabler for CF-mMIMO in future 6G networks. The proposed contributions combine theoretical analysis, algorithmic development, and system-level validation, offering practical and scalable solutions for next-generation wireless architectures.