Integrating channel knowledge map and deep reinforcement learning for optimizing RIS-assisted MU-MISO systems

IF 2.2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication Pub Date : 2025-07-11 DOI:10.1016/j.phycom.2025.102769

Mingkang Yang , Xingquan Li , Chunlong He

{"title":"Integrating channel knowledge map and deep reinforcement learning for optimizing RIS-assisted MU-MISO systems","authors":"Mingkang Yang , Xingquan Li , Chunlong He","doi":"10.1016/j.phycom.2025.102769","DOIUrl":null,"url":null,"abstract":"<div><div>A method that combines technical channel knowledge mapping (CKM) with deep reinforcement learning (DRL) to jointly optimize the phase offsets for transmission beamforming and reconfigurable intelligent surfaces (RIS) is developed in multi-user multiple-input single-output (MU-MISO) systems. The objective of the scheme is to enhance the overall downlink capacity under phase-sensitive reflection amplitude modeling conditions. The initial phase of the research involves pre-training using CKM to construct a model capable of accounting for positioning errors. Subsequently, the model will be migrated to a real scenario and formally trained based on the channel information that are obtained from channel estimation with channel estimation error. The proposed approach can efficiently exploit the environmental information and thus improve the performance and robustness of wireless communication systems. The results show that our approach has the potential to address the challenges of channel knowledge acquisition in hardware-constrained RIS-assisted wireless communication systems.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"72 ","pages":"Article 102769"},"PeriodicalIF":2.2000,"publicationDate":"2025-07-11","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/S1874490725001727","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

A method that combines technical channel knowledge mapping (CKM) with deep reinforcement learning (DRL) to jointly optimize the phase offsets for transmission beamforming and reconfigurable intelligent surfaces (RIS) is developed in multi-user multiple-input single-output (MU-MISO) systems. The objective of the scheme is to enhance the overall downlink capacity under phase-sensitive reflection amplitude modeling conditions. The initial phase of the research involves pre-training using CKM to construct a model capable of accounting for positioning errors. Subsequently, the model will be migrated to a real scenario and formally trained based on the channel information that are obtained from channel estimation with channel estimation error. The proposed approach can efficiently exploit the environmental information and thus improve the performance and robustness of wireless communication systems. The results show that our approach has the potential to address the challenges of channel knowledge acquisition in hardware-constrained RIS-assisted wireless communication systems.

查看原文本刊更多论文

整合渠道知识图谱和深度强化学习优化ris辅助MU-MISO系统

在多用户多输入单输出（MU-MISO）系统中，提出了一种将技术信道知识映射（CKM）与深度强化学习（DRL）相结合的方法，共同优化传输波束形成和可重构智能曲面（RIS）的相位偏移。该方案的目标是在相敏反射振幅建模条件下提高下行链路的总体容量。研究的初始阶段包括使用CKM进行预训练，以构建能够考虑定位误差的模型。随后，将模型迁移到真实场景中，并根据信道估计误差得到的信道信息进行正式训练。该方法可以有效地利用环境信息，从而提高无线通信系统的性能和鲁棒性。结果表明，我们的方法有潜力解决硬件受限的ris辅助无线通信系统中信道知识获取的挑战。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Physical Communication ENGINEERING, ELECTRICAL & ELECTRONICTELECO-TELECOMMUNICATIONS

CiteScore

5.00

自引率

9.10%

发文量

212

审稿时长

55 days

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.