OPTIMISATION IN NOMA WIRELESS COMMUNICATION NETWORKS

Abstract

With the exponential expansion of the Internet of Things (IoT) and the increasing demand for multimedia applications, the upcoming sixth-generation (6G) wireless communication network is poised to revolutionise connectivity. Non-orthogonal multiple access (NOMA) technique has been extensively studied in recent years because of the higher spectrum efficiency compared with orthogonal multiple access (OMA). NOMA enables multiple users and devices to share the same resource block, i.e., time slot, bandwidth and code, simultaneously, where the spectrum efficiency is improved. Furthermore, two innovative techniques, known as reconfigurable intelligent surface (RIS) and backscattering (BAC), have aroused people's interest. RIS has the capability to dynamically reconfigure the channel, enhancing signal quality, while BAC enables passive devices to transmit signals without consuming energy. Both of these techniques hold significant potential in IoT networks. This thesis focuses on exploring various optimisation problems arising from different NOMA scenarios to enhance the system's performance. First, a RIS-assisted NOMA downlink network, where multiple users receive signals from the base station (BS) with the help of multiple RISs, is investigated. Second, sum rate masmisation problem is formulated of multiple users in a RIS-assisted downlink NOMA network, where reinforcement learning is utilised as a tool to solve the this optimisation problem. Third, the combination of backscatter communication (BackCom) and NOMA is investigated. Finally, we verify the feasibility of introducing a BAC device into a legacy NOMA network without compromising its performance. The findings of this thesis not only underscore the critical significance of optimisation within the realm of wireless communication but also vividly illustrate the remarkable strides in spectrum efficiency realized through the deployment of NOMA technology.

Date of Award	31 Dec 2023
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Daniel Ka Chun So (Supervisor) & Zhiguo Ding (Supervisor)