Signal Processing Algorithms for MIMO-NOMA Based 6G Networks

Student thesis: Phd


This thesis aims to enhance the {anticipated } performance of sixth-generation (6G) communication networks by integrating non-orthogonal multiple access (NOMA) and multiple-input multiple-output (MIMO) techniques using the generalized singular value decomposition (GSVD)-based linear beamforming method. To do so, the thesis explores different scenarios with specific objectives, including minimizing mobile edge computing (MEC) offloading delay, maximizing the sum secrecy rate in a MIMO-NOMA system, minimizing the total energy consumption of a MIMO-NOMA-MEC system, and maximizing the sum data rate of the secondary network in a cognitive radio (CR)-based NOMA-MIMO system. To minimize the offloading delay in a MIMO-MEC system, the Dinkelbach transform and the GSVD method are employed. Analytical and simulation-based evaluations are conducted to assess the performance of the proposed Hybrid-NOMA-MIMO-MEC system. The simulation results show that this system achieves superior delay performance and lower energy consumption than conventional orthogonal multiple access (OMA) approaches. To improve the sum rate of confidential transmission in an uplink MIMO-NOMA system, the thesis focuses on maximizing the secrecy sum rate (SSR). By leveraging the GSVD method and first-order Taylor approximation, a suboptimal concave problem formulation is derived to tackle the non-convex nature of the SSR problem. The SSR is compared with other algorithms, including conventional orthogonal multiple access, and the simulation results demonstrate the effectiveness of the proposed method. To minimize the total energy consumption of local computing, task offloading, and MEC computing in a NOMA-MIMO-based system, the base station optimizes power allocation vectors and task assignment coefficients under time and power constraints. The non-convex problem is addressed through successive convex optimization (SCA) and alternating optimization (AO) techniques. The impact of various factors, such as delay tolerance, task size, and user distance, on energy consumption is investigated. Simulation results indicate that the proposed method outperforms orthogonal multiple access (OMA) schemes, particularly for large data sizes and stringent delay requirements. Finally, the thesis presents a novel approach for wireless-powered NOMA-MIMO systems. This approach is designed for cognitive underlay radio (CR) scenarios where the primary network requires predefined QoS. Given that requirement, the main objective is to maximize the sum rate of the secondary network. A joint beamforming vector for primary and secondary networks and a time-switching coefficient for energy harvesting and information transfer are optimized to achieve this objective. The problem formulation is non-convex. Therefore, we use of semi-definite programming, successive convex approximation, and alternating optimization techniques to solve this problem. The simulation results show that the NOMA-based solution outperforms the TDMA-based benchmark scheme, particularly at low transmit power levels. In conclusion, this thesis investigates integrating NOMA and MIMO technologies in 6G networks. It addresses delay minimization, maximization of secrecy sum rate, energy consumption optimization, and sum rate in CR scenarios. The proposed solutions demonstrate significant improvements in spectral efficiency, energy efficiency, data rate, and overall system performance, making them valuable contributions to the field of 6G communication networks.
Date of Award31 Dec 2023
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorEmad Alsusa (Supervisor) & Zhiguo Ding (Supervisor)


  • Energy minimization
  • Alternating optimization
  • Semi-definite programming
  • Generalized singular value decomposition
  • Sum rate maximization
  • Cognitive radio
  • Semi-definite relaxation
  • Delay minimization
  • Mobile edge computing
  • Physical layer security
  • Multiple-input multiple-output
  • Non-orthogonal multiple access
  • 6G Networks
  • Secrecy sum rate maximization

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