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

2025-04-01

AI Summary

To address the 6G requirements of high spectral efficiency, wide coverage, and low interference, this work investigates joint resource allocation in STAR-RIS-empowered CoMP-NOMA networks, aiming to dynamically guarantee user QoS while maximizing network sum rate under time-varying channels. We propose a novel system model that deeply integrates STAR-RIS, Coordinated Multipoint (CoMP), and Non-Orthogonal Multiple Access (NOMA) for the first time. Furthermore, we design a deep reinforcement learning (DRL)-based framework jointly optimizing energy efficiency and QoS, incorporating a real-time, adaptive decision-making mechanism tailored for aerial RIS deployment. Evaluated in a typical urban scenario, the proposed scheme achieves a 37% increase in network sum rate, a 3.2× improvement in edge-user throughput, a 58% reduction in outage probability, and a 22% decrease in base station power consumption—demonstrating significant advancement toward intelligent wireless resource management for 6G.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multi-Robot Systems
• Search and Optimization: Combinatorial Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Optimizing resource allocation in RIS-assisted CoMP-NOMA networks
• Enhancing spectral efficiency and coverage using STAR-RIS technology
• Applying DRL for adaptive optimization in aerial RIS networks

Innovation

• STAR-RIS deployment for interference mitigation
• Energy-efficient RIS optimization algorithms
• DRL for adaptive aerial RIS optimization