The Application of Optimization Techniques in BackCom NOMA Networks

Abstract

The integration of backscatter communication (BackCom) and non-orthogonal multiple access (NOMA) has emerged as a promising solution to address the growing demands of the Internet of things (IoT) by enabling both energy and spectrum cooperation among devices. BackCom facilitates energy harvesting and low-power communication, while NOMA allows multiple users to share the same frequency resources through power domain multiplexing, significantly enhancing spectral efficiency. The combination of these technologies offers considerable potential for advancing wireless communication networks, particularly in scenarios requiring efficient resource utilization and robust performance under constrained conditions. Optimization algorithms play a critical role in enhancing the performance of BackCom NOMA systems. In particular, the optimization of energy efficiency (EE) and sum uplink data rate is of great importance to ensure sustainable and high-performance communication. By employing advanced techniques such as semidefinite relaxation (SDR), successive convex approximation (SCA), and the Dinkelbach method, these optimization problems can be effectively addressed, leading to significant improvements in system efficiency. Hybrid successive interference cancellation (SIC) is employed to reduce the outage probability and enhance the overall uplink sum data rate. Additionally, the use of robust optimization methods, including the S-procedure and Bernstein-type inequality (BTI), further enhances the system’s reliability and robustness, particularly in the presence of channel imperfections. The research presented in this thesis contributes to the field by addressing several key challenges in BackCom NOMA systems. In the first study, the beamforming matrix is optimized for the first time in a BackCom NOMA network, focusing on maximizing the uplink data rate while ensuring downlink quality of service. The second study extends this work by simultaneously optimizing the beamforming matrix and reflection coefficients using two distinct decoding methods to achieve the highest EE. The third study introduces a novel multi-cell BackCom NOMA model, optimizing the sum uplink data rate through matching theory and quadratic transformation algorithms. Finally, the fourth study addresses the optimization of EE under imperfect channel conditions, extending the previous work on robust optimization techniques to further enhance system stability and performance. These contributions significantly advance the understanding and application of BackCom and NOMA in modern communication networks.

Date of Award	1 Aug 2025
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Daniel Ka Chun So (Supervisor) & Zhiguo Ding (Supervisor)