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Resource Allocation for RIS-Assisted CoMP-NOMA Networks using Reinforcement Learning

Muhammad Umer, Muhammad Ahmed Mohsin, Huma Ghafoor, Syed Ali Hassan

TL;DR

This work addresses the challenge of achieving high data rates and broad coverage in future wireless networks by integrating STAR-RIS, CoMP, and NOMA. It develops analytical frameworks to quantify performance gains (ergodic rate and outage probability) in STAR-RIS assisted CoMP-NOMA networks, and introduces energy-efficient designs with two passive beamforming configurations. A key contribution is the DRL-based optimization for aerial RIS deployments using MO-PPO to jointly optimize UAV trajectory, RIS phase shifts, and NOMA power control, demonstrating convergence and near-optimality compared with exhaustive search. The results show significant improvements in rate and reliability with STAR-RIS, especially for edge users, and validate the feasibility of DRL-driven adaptive optimization in dynamic RIS-enabled multi-cell networks, paving the way for scalable, energy-efficient 6G implementations.

Abstract

This thesis delves into the forefront of wireless communication by exploring the synergistic integration of three transformative technologies: STAR-RIS, CoMP, and NOMA. Driven by the ever-increasing demand for higher data rates, improved spectral efficiency, and expanded coverage in the evolving landscape of 6G development, this research investigates the potential of these technologies to revolutionize future wireless networks. The thesis analyzes the performance gains achievable through strategic deployment of STAR-RIS, focusing on mitigating inter-cell interference, enhancing signal strength, and extending coverage to cell-edge users. Resource sharing strategies for STAR-RIS elements are explored, optimizing both transmission and reflection functionalities. Analytical frameworks are developed to quantify the benefits of STAR-RIS assisted CoMP-NOMA networks under realistic channel conditions, deriving key performance metrics such as ergodic rates and outage probabilities. Additionally, the research delves into energy-efficient design approaches for CoMP-NOMA networks incorporating RIS, proposing novel RIS configurations and optimization algorithms to achieve a balance between performance and energy consumption. Furthermore, the application of Deep Reinforcement Learning (DRL) techniques for intelligent and adaptive optimization in aerial RIS-assisted CoMP-NOMA networks is explored, aiming to maximize network sum rate while meeting user quality of service requirements. Through a comprehensive investigation of these technologies and their synergistic potential, this thesis contributes valuable insights into the future of wireless communication, paving the way for the development of more efficient, reliable, and sustainable networks capable of meeting the demands of our increasingly connected world.

Resource Allocation for RIS-Assisted CoMP-NOMA Networks using Reinforcement Learning

TL;DR

This work addresses the challenge of achieving high data rates and broad coverage in future wireless networks by integrating STAR-RIS, CoMP, and NOMA. It develops analytical frameworks to quantify performance gains (ergodic rate and outage probability) in STAR-RIS assisted CoMP-NOMA networks, and introduces energy-efficient designs with two passive beamforming configurations. A key contribution is the DRL-based optimization for aerial RIS deployments using MO-PPO to jointly optimize UAV trajectory, RIS phase shifts, and NOMA power control, demonstrating convergence and near-optimality compared with exhaustive search. The results show significant improvements in rate and reliability with STAR-RIS, especially for edge users, and validate the feasibility of DRL-driven adaptive optimization in dynamic RIS-enabled multi-cell networks, paving the way for scalable, energy-efficient 6G implementations.

Abstract

This thesis delves into the forefront of wireless communication by exploring the synergistic integration of three transformative technologies: STAR-RIS, CoMP, and NOMA. Driven by the ever-increasing demand for higher data rates, improved spectral efficiency, and expanded coverage in the evolving landscape of 6G development, this research investigates the potential of these technologies to revolutionize future wireless networks. The thesis analyzes the performance gains achievable through strategic deployment of STAR-RIS, focusing on mitigating inter-cell interference, enhancing signal strength, and extending coverage to cell-edge users. Resource sharing strategies for STAR-RIS elements are explored, optimizing both transmission and reflection functionalities. Analytical frameworks are developed to quantify the benefits of STAR-RIS assisted CoMP-NOMA networks under realistic channel conditions, deriving key performance metrics such as ergodic rates and outage probabilities. Additionally, the research delves into energy-efficient design approaches for CoMP-NOMA networks incorporating RIS, proposing novel RIS configurations and optimization algorithms to achieve a balance between performance and energy consumption. Furthermore, the application of Deep Reinforcement Learning (DRL) techniques for intelligent and adaptive optimization in aerial RIS-assisted CoMP-NOMA networks is explored, aiming to maximize network sum rate while meeting user quality of service requirements. Through a comprehensive investigation of these technologies and their synergistic potential, this thesis contributes valuable insights into the future of wireless communication, paving the way for the development of more efficient, reliable, and sustainable networks capable of meeting the demands of our increasingly connected world.

Paper Structure

This paper contains 89 sections, 7 theorems, 55 equations, 17 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Project Overview
  3. Motivation
  4. Objectives
  5. Methodology
  6. Project Outcomes
  7. Organization of the Report
  8. Background
  9. Preliminaries
  10. Challenges in Modern Wireless Communication
  11. Emerging Technologies
  12. Non-Orthogonal Multiple Access (NOMA)
  13. Coordinated Multi-Point (CoMP)
  14. Reconfigurable Intelligent Surfaces (RIS)
  15. Simultaneously Transmitting and Reflecting RIS (STAR-RIS)
  16. ...and 74 more sections

Key Result

Lemma 1

Assuming a large $K$, and by applying MoM, the distribution of $Z_{i,u}$ is approximated as a Gamma distribution, $Z_{i,u} \sim \Gamma(k_{Z_{i,u}}, \theta_{Z_{i,u}})$, with the following probability density function (PDF). where $k_{Z_{i,u}} = \frac{\mu_{Z_{i,u}}^2}{\mu_{Z_{i,u}}^{(2)} - \mu_{Z_{i,u}}^2}$ and $\theta_{Z_{i,u}} = \frac{\mu_{Z_{i,u}}^{(2)} - \mu_{Z_{i,u}}^2}{\mu_{Z_{i,u}}}$ are the

Figures (17)

  • Figure 1: An illustration of STAR-RIS-aided coordinated NOMA cluster.
  • Figure 2: The PDFs and CDFs of the SINRs at the center and edge user, with $K=34$ elements, $m_{i,u}=m_{i',u}=1$, and $m_{i,R}=m_{R,u}=2$, $\forall i \in \mathcal{I}, i' \in \mathcal{I} \setminus \{i\}$, $\forall u \in \mathcal{U}$.
  • Figure 3: The PDFs and CDFs of the SINRs at the center and edge user, with $K=34$ elements, $m_{i,u}=m_{i',u}=1$, and $m_{i,R}=m_{R,u}=2$, $\forall i \in \mathcal{I}, i' \in \mathcal{I} \setminus \{i\}$, $\forall u \in \mathcal{U}$.
  • Figure 4: Outage probability of network users versus $P_{t}$ for equal amplitude coefficients $(\beta^t=\beta^r)$, and element assignments $(\textbf{K}_R^1=\textbf{K}_R^2)$, when $K>0$.
  • Figure 5: Ergodic rate for varying RIS element assignments $(\textbf{K}_R^1,\,\textbf{K}_R^2)$ and amplitude adjustments $(\beta_t,\,\beta_r)$, with $P_t =-10$ dBm.
  • ...and 12 more figures

Theorems & Definitions (13)

  • Lemma 1
  • proof
  • Lemma 2
  • proof
  • Lemma 3
  • proof
  • Corollary 1
  • Lemma 4
  • proof
  • Theorem 1
  • ...and 3 more