通信系统中具有自适应定制RIS相位的学习型智能识别器
Learned Intelligent Recognizer with Adaptively Customized RIS Phases in Communication Systems
AI总结：
本文提出一种基于LSTM和物理信道模型的智能识别器，通过复用下行信号并自适应定制RIS相位，在RIS辅助通信系统中实现高效目标识别，性能显著优于现有方法且对通信影响极小。
摘要：
本研究提出了一种先进的无线系统，该系统将目标识别嵌入到可重构智能表面（RIS）辅助的通信系统中，并由前沿的深度学习创新技术驱动。此类系统面临同时微调RIS相移和神经网络（NN）参数的挑战，因为二者在完成识别任务时存在复杂的相互依赖关系。为应对这些挑战，我们提出了一种智能识别器，该识别器策略性地利用每一条先验动作响应，从而巧妙地复用下行链路信号以促进环境感知。具体而言，我们基于长短期记忆（LSTM）架构和物理信道模型设计了一种新型神经网络。该神经网络迭代地捕获并融合来自先前测量的信息，并自适应地定制RIS配置，以在后续时刻获取对识别任务最相关的信息。这些配置根据场景、任务和目标特性进行动态定制。仿真结果表明，我们提出的方法显著优于最先进的方法，同时在同时进行感知的情况下对通信性能的影响极小。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
