标题： 具有自适应定制RIS相位的学习智能识别器在通信系统中的应用 显示英文标题

标题： Learned Intelligent Recognizer with Adaptively Customized RIS Phases in Communication Systems

摘要： 本研究提出了一种先进的无线系统，该系统在可重构智能表面（RIS）辅助通信系统中嵌入了目标识别功能，并通过尖端的深度学习创新来实现。 由于RIS相位偏移和神经网络（NN）参数相互之间存在复杂依赖关系，因此此类系统面临微调这些参数的挑战。 为了解决这些挑战，我们提出了一种智能识别器，该识别器战略性地利用每一次先前动作响应，从而巧妙地多路复用下行信号以促进环境感知。 具体而言，我们设计了一种基于长短期记忆（LSTM）架构和物理信道模型的新神经网络。 该神经网络迭代捕获并融合从前一次测量中获得的信息，并自适应地定制RIS配置，以便在随后的时刻获取与识别任务最相关的信息。 这些配置根据场景、任务和目标的具体情况动态调整。 仿真结果显示，我们提出的方法显著优于现有方法，同时对通信性能的影响极小，即使在同时进行感知时也是如此。 显示英文摘要

摘要： This study presents an advanced wireless system that embeds target recognition within reconfigurable intelligent surface (RIS)-aided communication systems, powered by cuttingedge deep learning innovations. Such a system faces the challenge of fine-tuning both the RIS phase shifts and neural network (NN) parameters, since they intricately interdepend on each other to accomplish the recognition task. To address these challenges, we propose an intelligent recognizer that strategically harnesses every piece of prior action responses, thereby ingeniously multiplexing downlink signals to facilitate environment sensing. Specifically, we design a novel NN based on the long short-term memory (LSTM) architecture and the physical channel model. The NN iteratively captures and fuses information from previous measurements and adaptively customizes RIS configurations to acquire the most relevant information for the recognition task in subsequent moments. Tailored dynamically, these configurations adapt to the scene, task, and target specifics. Simulation results reveal that our proposed method significantly outperforms the state-of-the-art method, while resulting in minimal impacts on communication performance, even as sensing is performed simultaneously.