{"title":"DRL-based RIS-assisted Integrated Sensing and Communication Joint design of beamforming and reflection phase shift","authors":"Zaiqiang Wang , Zhongqiang Luo , Yiting Lei","doi":"10.1016/j.phycom.2025.102669","DOIUrl":null,"url":null,"abstract":"<div><div>As one of the key application scenarios of the sixth generation of mobile communications (6G), various industries have partially researched Integrated Sensing and Communication (ISAC). ISAC can combine the functions of communications and radar to achieve the sharing of the hardware platform and unlimited radio spectrum. In the complex environment of the ISAC system, when encountering signal fading and energy loss, reconfigurable intelligent surfaces (RIS) can be used to assist ISAC communications, providing a solution for efficient communications and high-precision sensing for future 6G systems. Currently, most joint beamforming designs for RIS-assisted ISAC systems adopt traditional methods. The core idea is to achieve resource allocation and beam steering through mathematical modeling and optimization theory. This study proposes a joint beamforming design scheme using deep reinforcement learning (DRL). Based on the DRL framework, the soft actor-critic (SAC) algorithm is adopted to achieve a low-complexity scheme to solve the non-convex optimization problem in RIS-assisted ISAC systems. Simulation results show the effectiveness of the DRL-based method in RIS-assisted ISAC systems and demonstrate that the proposed algorithm can maximize the guaranteed communication rate of users while achieving perception of the surrounding environment.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"71 ","pages":"Article 102669"},"PeriodicalIF":2.0000,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical Communication","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1874490725000722","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
As one of the key application scenarios of the sixth generation of mobile communications (6G), various industries have partially researched Integrated Sensing and Communication (ISAC). ISAC can combine the functions of communications and radar to achieve the sharing of the hardware platform and unlimited radio spectrum. In the complex environment of the ISAC system, when encountering signal fading and energy loss, reconfigurable intelligent surfaces (RIS) can be used to assist ISAC communications, providing a solution for efficient communications and high-precision sensing for future 6G systems. Currently, most joint beamforming designs for RIS-assisted ISAC systems adopt traditional methods. The core idea is to achieve resource allocation and beam steering through mathematical modeling and optimization theory. This study proposes a joint beamforming design scheme using deep reinforcement learning (DRL). Based on the DRL framework, the soft actor-critic (SAC) algorithm is adopted to achieve a low-complexity scheme to solve the non-convex optimization problem in RIS-assisted ISAC systems. Simulation results show the effectiveness of the DRL-based method in RIS-assisted ISAC systems and demonstrate that the proposed algorithm can maximize the guaranteed communication rate of users while achieving perception of the surrounding environment.
期刊介绍:
PHYCOM: Physical Communication is an international and archival journal providing complete coverage of all topics of interest to those involved in all aspects of physical layer communications. Theoretical research contributions presenting new techniques, concepts or analyses, applied contributions reporting on experiences and experiments, and tutorials are published.
Topics of interest include but are not limited to:
Physical layer issues of Wireless Local Area Networks, WiMAX, Wireless Mesh Networks, Sensor and Ad Hoc Networks, PCS Systems; Radio access protocols and algorithms for the physical layer; Spread Spectrum Communications; Channel Modeling; Detection and Estimation; Modulation and Coding; Multiplexing and Carrier Techniques; Broadband Wireless Communications; Wireless Personal Communications; Multi-user Detection; Signal Separation and Interference rejection: Multimedia Communications over Wireless; DSP Applications to Wireless Systems; Experimental and Prototype Results; Multiple Access Techniques; Space-time Processing; Synchronization Techniques; Error Control Techniques; Cryptography; Software Radios; Tracking; Resource Allocation and Inference Management; Multi-rate and Multi-carrier Communications; Cross layer Design and Optimization; Propagation and Channel Characterization; OFDM Systems; MIMO Systems; Ultra-Wideband Communications; Cognitive Radio System Architectures; Platforms and Hardware Implementations for the Support of Cognitive, Radio Systems; Cognitive Radio Resource Management and Dynamic Spectrum Sharing.