论文标题
智能反映智能收音机的智能反射表面配置，使用深度加固学习
Intelligent Reflecting Surface Configurations for Smart Radio Using Deep Reinforcement Learning
论文作者
论文摘要
设想智能反射表面（IRS）将无线通信的范式从“适应到无线通道”更改为“更改无线通道”。但是，当前的IRS配置方案包括子渠道估计和被动式序列形成，符合传统的基于模型的设计理念，并且在复杂的无线电环境中很难实现。为了创建智能无线电环境，我们建议对IRS控制的无模型设计，该设计独立于子渠道通道状态信息（CSI），并需要IRS与无线通信系统之间的最小互动。首先，我们将对IRS的控制作为Markov决策过程（MDP）进行建模，并应用深入增强学习（DRL）来执行IRS的实时粗相控制。然后，我们将寻求控制（ESC）的极值寻求（ESC）作为IRS的良好相控制。最后，通过更新框架结构，我们将DRL和ESC集成在IRS的无模型控制中，以提高其对不同信道动态的适应性。数值结果表明我们提出的关节DRL和ESC方案的优越性，并验证其在没有子渠道CSI的无模型IRS控制中的有效性。
Intelligent reflecting surface (IRS) is envisioned to change the paradigm of wireless communications from "adapting to wireless channels" to "changing wireless channels". However, current IRS configuration schemes, consisting of sub-channel estimation and passive beamforming in sequence, conform to the conventional model-based design philosophies and are difficult to be realized practically in the complex radio environment. To create the smart radio environment, we propose a model-free design of IRS control that is independent of the sub-channel channel state information (CSI) and requires the minimum interaction between IRS and the wireless communication system. We firstly model the control of IRS as a Markov decision process (MDP) and apply deep reinforcement learning (DRL) to perform real-time coarse phase control of IRS. Then, we apply extremum seeking control (ESC) as the fine phase control of IRS. Finally, by updating the frame structure, we integrate DRL and ESC in the model-free control of IRS to improve its adaptivity to different channel dynamics. Numerical results show the superiority of our proposed joint DRL and ESC scheme and verify its effectiveness in model-free IRS control without sub-channel CSI.
