Learned Intelligent Recognizer with Adaptively Customized RIS Phases in Communication Systems

2025-05-05

AI Summary

In RIS-aided communication systems, the strong coupling between RIS phase configuration and neural network parameters hinders joint optimization. Method: This paper proposes an integrated communication-and-sensing framework featuring an iterative sensing architecture that tightly fuses LSTM networks with a physics-based channel model, enabling end-to-end joint optimization of RIS phase responses and deep neural network parameters. The method dynamically customizes RIS phase profiles in real time based on scene, task, and objective characteristics—without requiring additional time-frequency resources. Contribution/Results: Experiments demonstrate that the proposed approach significantly outperforms state-of-the-art methods in target recognition accuracy while preserving nearly all communication throughput. It achieves, for the first time, zero-overhead, real-time, high-accuracy embedded sensing—fully integrating sensing functionality into the communication infrastructure without performance trade-offs.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Optimizing RIS phase shifts and NN parameters interdependently
• Multiplexing downlink signals for environment sensing intelligently
• Adapting RIS configurations dynamically for target recognition

Innovation

• Uses LSTM-based NN for adaptive RIS configuration
• Multiplexes downlink signals for environment sensing
• Dynamically tailors RIS to scene and task