Mobile Reconfigurable Intelligent Surfaces for NOMA Networks: Federated Learning Approaches
Mobile Reconfigurable Intelligent Surfaces for NOMA Networks: Federated Learning Approaches
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DOI：
10.1109/twc.2022.3181747
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发表时间：
2021-03
影响因子：
10.4
通讯作者：
Ruikang Zhong;Xiao Liu;Yuanwei Liu;Yue Chen;Zhu Han
中科院分区：
文献类型：
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作者：
Ruikang Zhong;Xiao Liu;Yuanwei Liu;Yue Chen;Zhu Han
提出了一个可重新配置智能表面（RISS）增强室内无线网络的新颖框架，其中调用了安装在机器人上的RIS以启用RIS的移动性并提高移动用户的服务质量。同时，采用了非正交的多重访问（NOMA）技术来进一步提高频谱效率，因为RIS能够为Noma提供人工控制的通道条件，这可以看作是获得Noma增益的有益操作条件。为了优化所有用户的总和，调用了深层确定性的策略梯度（DDPG）算法，以优化移动RIS的部署和相移以及电源分配策略。为了提高DDPG代理的代理培训的效率和有效性，采用联合学习（FL）概念来使多个代理人同时探索类似的环境和交换经验。我们还证明，通过相同的随机探索政策，佛罗里达州武装的深钢筋学习（DRL）代理可以从理论上获得与独立代理人相比的奖励增益。我们的仿真结果表明，移动RIS方案可以显着胜过固定的RIS范式，与固定的RIS范式相比，该范式的数据速率增长约为三倍。此外，与OMA方案相比，NOMA方案能够在总和率方面获得42％的增益。最后，多细胞模拟证明，FL的增强DDPG算法比独立训练框架具有较高的收敛速率和优化性能。
A novel framework of reconfigurable intelligent surfaces (RISs)-enhanced indoor wireless networks is proposed, where an RIS mounted on the robot is invoked to enable mobility of the RIS and enhance the service quality for mobile users. Meanwhile, non-orthogonal multiple access (NOMA) techniques are adopted to further increase the spectrum efficiency since RISs are capable of providing NOMA with artificial controlled channel conditions, which can be seen as a beneficial operation condition to obtain NOMA gains. To optimize the sum rate of all users, a deep deterministic policy gradient (DDPG) algorithm is invoked to optimize the deployment and phase shifts of the mobile RIS as well as the power allocation policy. In order to improve the efficiency and effectiveness of agent training for the DDPG agents, a federated learning (FL) concept is adopted to enable multiple agents to simultaneously explore similar environments and exchange experiences. We also proved that with the same random exploring policy, the FL armed deep reinforcement learning (DRL) agents can theoretically obtain a reward gain comparing to the independent agents. Our simulation results indicate that the mobile RIS scheme can significantly outperform the fixed RIS paradigm, which provides about three times data rate gain compared to the fixed RIS paradigm. Moreover, the NOMA scheme is capable of achieving a gain of 42% in contrast with the OMA scheme in terms of the sum rate. Finally, the multi-cell simulation proved that the FL enhanced DDPG algorithm has a superior convergence rate and optimization performance than the independent training framework.