Optimization of Intelligent Reflecting Surfaces Enabled Future Wireless Communication Technologies: Network Coding, NOMA, and Integrated Sensing and Communications

Abstract

The future landscape of 6G networks underscores increasing demands for both high data rate as well as low-energy wireless solutions. As data demands increase rapidly, the power consumption of the wireless networks increase too. Therefore, achieving a harmonious balance between high-speed data transmission and sustainable energy practices is essential to meet the evolving digital needs while ensuring reduced power consumption levels. Energy efficiency is indispensable for energy autonomous networks such as Internet of Things (IoT) and unmanned aerial vehicle (UAV) networks, where the base station (BS), possibly attached to a drone, and users have limited hardware capabilities and energy budget. Thus, developing both spectral and energy efficient wireless technologies is crucial to meet the requirements of future networks. Hence, in this thesis, we exploit emerging wireless technologies such as intelligent reflective surfaces (IRS), physical layer network coding (PNC), non-orthogonal multiple access (NOMA), and massive multiple input multiple output (MIMO) to design energy and spectral efficient data exchange and multiple access techniques for future networks. For instance, since PNC is a spectral efficient data exchange technique, exploiting the IRS technology to enhance the performance of PNC is thoroughly studied in this thesis where performance analyses as well as system optimization are provided. Furthermore, exploiting IRS to enhance the performance of uplink NOMA as an energy efficient multiple access technique is investigated. Low complexity and efficient optimization techniques are provided with the objective of minimizing the total power consumption of IRS-assisted uplink NOMA system while satisfying the quality of service (QoS) constraints. Furthermore, since sensing services are expected to be an integral part of future 6G networks, then the performance of IRS-assisted integrated sensing and communications (ISAC) is also studied. Robust active-passive beamforming optimization algorithms is provided to enhance the performance of IRS-enabled ISAC system in the presence of channel uncertainty. Finally, since massive MIMO is a spectral efficient multiple technique which will be an integral part of future networks, we study its performance in practical systems where perfect channel state information (CSI) is not available at BS. Specifically, we study the channel feedback problem in frequency division duplex (FDD) massive MIMO systems with multiple antennas at the users, where two efficient feedback schemes, digital and analog feedback, are proposed and analyzed. Lastly, extensive simulation results are provided for all the presented system models in the thesis to prove the efficacy of the proposed theoretical analyses and optimization algorithms.

Date of Award	22 May 2024
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Khairi Hamdi (Co Supervisor) & Emad Alsusa (Main Supervisor)