[论文解读] RIS Enhanced Massive Non-orthogonal Multiple Access Networks: Deployment and Passive Beamforming Design

Xiao Liu, Yuanwei Liu+2 more|arXiv (Cornell University)|Jan 28, 2020

Advanced Wireless Communication Technologies参考文献 54被引用 11

一句话总结

该论文提出了一种深度强化学习框架，用于在RIS增强的海量NOMA网络中联合优化部署与无源波束成形，以最大化能量效率。它采用基于LSTM的ESN进行流量预测，并使用基于D³QN的智能体优化RIS位置与相位偏移，相较于OMA和基准RIS部署方案，实现了更优的能量效率。

ABSTRACT

A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS) with the aid of non-orthogonal multiple access (NOMA) technology. The problem of joint deployment, phase shift design, as well as power allocation is formulated for maximizing the energy efficiency with considering users' particular data requirements. To tackle this pertinent problem, machine learning approaches are adopted in two steps. Firstly, a novel long short-term memory (LSTM) based echo state network (ESN) algorithm is proposed to predict users' tele-traffic demand by leveraging a real dataset. Secondly, a decaying double deep Q-network (D3QN) based position-acquisition and phase-control algorithm is proposed to solve the joint problem of deployment and design of the RIS. In the proposed algorithm, the base station, which controls the RIS by a controller, acts as an agent. The agent periodically observes the state of the RIS-enhanced system for attaining the optimal deployment and design policies of the RIS by learning from its mistakes and the feedback of users. Additionally, it is proved that the proposed D3QN based deployment and design algorithm is capable of converging within mild conditions. Simulation results are provided for illustrating that the proposed LSTM-based ESN algorithm is capable of striking a tradeoff between the prediction accuracy and computational complexity. Finally, it is demonstrated that the proposed D3QN based algorithm outperforms the benchmarks, while the NOMA-enhanced RIS system is capable of achieving higher energy efficiency than orthogonal multiple access (OMA) enabled RIS system.

研究动机与目标

• 解决在大规模NOMA网络中优化RIS部署与无源波束成形以提升能量效率的挑战。
• 在真实无线环境中，考虑用户动态数据速率需求与变化的流量需求。
• 在服务质量约束下，提出联合优化RIS部署、相位偏移设计与功率分配的框架。
• 通过机器学习预测用户流量，并利用强化学习实现实时自适应RIS控制。
• 通过对比性能评估，证明NOMA在RIS辅助系统中优于OMA。

提出的方法

• 提出一种基于LSTM的回声状态网络（ESN），利用实测数据预测用户实时通信流量需求。
• 设计一种衰减的双重深度Q网络（D³QN）算法，其中基站作为智能体，学习最优RIS部署与相位偏移策略。
• 将RIS部署与波束成形建模为马尔可夫决策过程，奖励基于系统能量效率与用户QoS满足度。
• 在NOMA中引入动态解码顺序，基于信道增益提升逐次干扰消除（SIC）性能。
• 采用实际的RIS能耗模型，包括电路功耗与反射功耗，以确保实际的能量效率优化。
• 采用双Q学习机制结合ε衰减策略，提升D³QN智能体的训练稳定性和收敛性。

实验结果

研究问题

• RQ1 基于LSTM的ESN模型能否在计算开销较低的前提下，准确预测真实RIS增强网络中的用户流量需求？
• RQ2 D³QN智能体相比标准DQN，在优化RIS部署与相位偏移以提升能量效率方面有何优势？
• RQ3 与固定顺序解码相比，NOMA中动态解码顺序对RIS辅助系统性能有何影响？
• RQ4 与随机或质心部署相比，通过D³QN学习到的最优位置部署RIS是否能显著提升系统能量效率？
• RQ5 RIS中反射单元数量的增加如何影响频谱效率与能量效率之间的权衡？

主要发现

• 所提出的基于LSTM的ESN在预测精度与计算复杂度之间实现了良好平衡，支持实时流量预测。
• D³QN算法在温和条件下实现收敛，通过双Q学习与ε衰减机制，性能优于标准DQN。
• 最优RIS部署显著提升能量效率，D³QN学习到的部署位置优于随机与质心部署。
• NOMA-RIS系统相比传统放大的中继（AF）方案，能量效率最高提升达300%，且优于基于OMA的RIS系统。
• 能量效率在反射单元数量约为18时达到峰值，超过该值后进一步增加导致能耗上升但性能无提升。
• 在用户数据需求相近时，NOMA中的动态解码顺序（NOMA-PH-optimal）优于固定顺序解码（NOMA-PH-random）。