机器学习在智能反射面辅助的通信系统中的应用综述  

Contemporary Survey of Machine Learning-based Approaches to Solving Communication Issues for Intelligent Reflecting Surfaces

作　　者：司鹏搏 李双缘 刘畅 李萌 SI Pengbo;LI Shuangyuan;LIU Chang;LI Meng(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;Beijing Laboratory of Advanced Information Networks,Beijing 100124,China)

机构地区：[1]北京工业大学信息学部,北京100124 [2]先进信息网络北京实验室,北京100124

出　　处：《北京工业大学学报》2025年第1期87-99,共13页Journal of Beijing University of Technology

基　　金：北京市自然科学基金资助项目(4244065);北京市博士后工作经费资助项目(2023-zz-98)。

摘　　要：智能反射面(intelligent reflecting surfaces,IRS)可以通过大量低成本的无源反射元件巧妙地调整信号反射,从而动态改变无线信道,提高通信性能,目前已成为无线通信研究的焦点。然而,由于IRS的加入,整个通信系统变得更加复杂,系统的动态性也更高,使通信系统面临着许多新的挑战。机器学习(machine learning,ML)具有很强的数据处理与自适应能力,能够不断适应变化的环境和需求,可以很好地应对通信系统中的许多挑战。因此,使用ML解决IRS辅助的通信系统中的问题,已经成为当前研究的重点。基于此,对ML在IRS系统中的应用进行了系统性的概述,从IRS辅助的通信系统中存在的问题入手,分别从反射因子的设计与优化、信道处理与建模、资源分配和管理、安全性增强4个方面对ML在IRS系统中的应用进行阐述和分析,并讨论了将ML应用在IRS系统中的优势及未来的发展趋势与挑战。Intelligent reflecting surfaces(IRS)can dynamically change the wireless channel and improve the communication performance by subtly adjusting the signal reflection through a large number of low-cost passive reflecting elements,which has become the focus of wireless communication research.However,due to the addition of IRS,the whole communication system becomes more complex and the dynamics of the system is higher,making the communication system face many new challenges.With a strong data processing and adaptive ability,machine learning(ML)can constantly adapt to changing environments and needs and well cope with many challenges in communication systems.Therefore,employing ML to tackle issues in IRS-assisted communication systems has emerged as a central theme of current research.Based on this,this paper gives a systematic overview of the application of ML in IRS system.Starting with the problems existing in IRS-assisted communication system,this paper expounds and analyzes the application of ML in IRS system from four aspects:design and optimization of reflection factor,channel processing and modeling,resource allocation and management,and security enhancement,and it also discusses the advantages and future development trends and challenges of applying ML in IRS system.

关 键 词：智能反射面(intelligent reflecting surfaces IRS) 无线通信 反射因子 信道 资源分配 通信安全 机器学习(machine learning ML) 

分 类 号：TN929.5[电子电信—通信与信息系统]