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 to provide 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 compare 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 compare to the fixed RIS paradigm. Moreover, the NOMA scheme is capable to achieve a gain of 42% in contrast with the OMA scheme in terms of 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.
翻译:提出了可重新整合的智能表面(RIS)增强室内无线网络的新框架,其中采用机器人上安装的RIS,以优化移动性RIS的部署和阶段转移以及电力分配政策。同时,采用非横向多重访问技术进一步提高频谱效率,因为RIS能够向NOMA提供人工控制的频道条件,这可以被视为获得NOMA收益的一个有利操作条件。为了优化所有用户的总和率,将采用深度确定性政策梯度算法(DPG)优化移动性RIS的部署和阶段转移以及电力分配政策。为提高DDPG代理商代理培训的效率和有效性,采用了非横向多重访问技术(NOMA)概念,使多个代理商能够同时探索类似环境和交流经验。我们还证明,根据同样的随机探索政策,FL深层武装强化学习(DRL)代理商在理论上可以获得与独立代理商的奖励。我们的模拟结果表明,移动性RIS计划可以大大超过移动性定义以及权力分配政策的阶段转移性变化率。在固定的IMISA模型中,将稳定性LMA的升级率与固定的升级率加以比较。