Reconfigurable intelligent surface (RIS) has recently gained popularity as a promising solution for improving the signal transmission quality of wireless communications with less hardware cost and energy consumption. This letter offers a novel deep reinforcement learning (DRL) algorithm based on a location-aware imitation environment for the joint beamforming design in an RIS-aided mmWave multiple-input multiple-output system. Specifically, we design a neural network to imitate the transmission environment based on the geometric relationship between the user’s location and the mmWave channel. Following this, a novel DRL-based method is developed that interacts with the imitation environment using the easily available location information. Finally, simulation results demonstrate that the proposed DRL-based algorithm provides more robust performance without excessive interaction overhead compared to the existing DRL-based approaches.

中文翻译：

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基于 RIS 辅助毫米波 MIMO 系统位置感知模仿环境的深度强化学习


可重构智能表面（RIS）最近作为一种有前景的解决方案而受到欢迎，该解决方案能够以更少的硬件成本和能耗提高无线通信的信号传输质量。这封信提供了一种基于位置感知模仿环境的新型深度强化学习 (DRL) 算法，用于 RIS 辅助毫米波多输入多输出系统中的联合波束成形设计。具体来说，我们设计了一个神经网络来根据用户位置与毫米波信道之间的几何关系来模拟传输环境。随后，开发了一种基于 DRL 的新颖方法，该方法使用易于获得的位置信息与模仿环境进行交互。最后，仿真结果表明，与现有的基于 DRL 的方法相比，所提出的基于 DRL 的算法提供了更鲁棒的性能，且没有过多的交互开销。