用强化学习优化6G中RIS辅助的协作多点传输网络资源分配。
Resource Allocation for RIS-Assisted CoMP-NOMA Networks using Reinforcement Learning
- 基于深度强化学习动态分配STAR-RIS的反射与透射资源。
- 在真实信道下实现用户速率提升与中断概率降低。
- 适合关注6G智能无线网络设计的研究者。
本论文聚焦无线通信前沿，探索星型可重构智能表面（STAR-RIS）、协作多点传输（CoMP）和非正交多址（NOMA）三类技术的协同集成。针对6G发展中对更高数据速率、更优频谱效率和更大覆盖范围的需求，研究分析了战略性部署STAR-RIS在缓解小区间干扰、增强信号强度及扩展边缘用户覆盖方面的性能增益。探讨了STAR-RIS单元的资源共享策略，优化其发射与反射功能。构建了适用于实际信道条件的分析框架，推导出无差错速率与中断概率等关键性能指标。此外，提出面向能源效率的CoMP-NOMA网络设计方法，包括新型RIS配置与优化算法，在性能与能耗之间取得平衡。进一步探索深度强化学习（DRL）在空基RIS辅助的CoMP-NOMA网络中的智能自适应优化应用，旨在最大化网络总速率的同时满足用户服务质量要求。通过系统性研究这些技术及其协同潜力，为未来无线通信发展提供重要洞见，推动更高效、可靠、可持续网络的构建。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。