{"title":"Beamforming and Phase Design for Active RIS-Aided Communications Using GBML","authors":"Jingming Tang;Nai Wu;Yaolian Song;Ziyi Jiang;Guicai Yu","doi":"10.1109/LCOMM.2025.3647094","DOIUrl":null,"url":null,"abstract":"In this letter, we investigate a multi-user communication network aided by active reconfigurable intelligent surface (RIS). To maximize the weighted sum rate (WSR) of the system, we formulate a joint transmit precoding and RIS coefficient optimization problem. However, the traditional alternating direction method of multipliers (ADMM) optimization approach for the problem suffers from high computational complexity and can only able to obtain suboptimal solutions. To deal with this issue, we propose a gradient-based meta-learning (GBML) scheme, which fuses meta-learning, neural network and nested iteration to improve the overall spectral efficiency and reduce the computational complexity associated with high-dimensional variables. Simulation results indicate that the proposed scheme can improve the WSR by up to 10.6%/70.25% in ideal/non-ideal scenarios compared with ADMM, and achieve a faster convergence rate as well.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"30 ","pages":"1036-1040"},"PeriodicalIF":4.5000,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Communications Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11343759/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
In this letter, we investigate a multi-user communication network aided by active reconfigurable intelligent surface (RIS). To maximize the weighted sum rate (WSR) of the system, we formulate a joint transmit precoding and RIS coefficient optimization problem. However, the traditional alternating direction method of multipliers (ADMM) optimization approach for the problem suffers from high computational complexity and can only able to obtain suboptimal solutions. To deal with this issue, we propose a gradient-based meta-learning (GBML) scheme, which fuses meta-learning, neural network and nested iteration to improve the overall spectral efficiency and reduce the computational complexity associated with high-dimensional variables. Simulation results indicate that the proposed scheme can improve the WSR by up to 10.6%/70.25% in ideal/non-ideal scenarios compared with ADMM, and achieve a faster convergence rate as well.
期刊介绍:
The IEEE Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of communication over different media and channels including wire, underground, waveguide, optical fiber, and storage channels. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of communication systems.