A Learning-Based Flexible Beamforming Method for Movable Antenna-Enabled Integrated Sensing, Communication, and Power Transmission System

IF 4.4 3区计算机科学 Q2 TELECOMMUNICATIONS

IEEE Communications Letters Pub Date : 2025-07-01 DOI:10.1109/LCOMM.2025.3584722

Chenfei Xie;Yonghui Li;Qingqing Tu;Yue Xiu;Songjie Yang;Zhenzhen Hu;Jing Jin;Zhongpei Zhang

{"title":"A Learning-Based Flexible Beamforming Method for Movable Antenna-Enabled Integrated Sensing, Communication, and Power Transmission System","authors":"Chenfei Xie;Yonghui Li;Qingqing Tu;Yue Xiu;Songjie Yang;Zhenzhen Hu;Jing Jin;Zhongpei Zhang","doi":"10.1109/LCOMM.2025.3584722","DOIUrl":null,"url":null,"abstract":"The Integrated Sensing, Communication, and Power Transmission (ISCPT) system is crucial for next-generation intelligent communication, enabling efficient spectrum management. By utilizing movable antennas (MAs), ISCPT enhances reliability and flexibility, supporting various intelligent Internet of Things (IoT) scenarios. While joint optimization of parameters like beamforming and antenna positioning improves performance, it also introduces computational complexity that may affect efficiency. To enable flexible beamforming, we propose a novel deep reinforcement learning (DRL) architecture where heterogeneous agents independently adjust antenna configurations. This approach improves sensing accuracy, communication reliability, and power transfer efficiency, enhancing system capabilities and adaptability to dynamic environments. This work lays the foundation for wireless systems that integrate intelligent communication, sensing, and power transfer, offering improved performance in real-world applications.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 9","pages":"2043-2047"},"PeriodicalIF":4.4000,"publicationDate":"2025-07-01","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/11062362/","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

The Integrated Sensing, Communication, and Power Transmission (ISCPT) system is crucial for next-generation intelligent communication, enabling efficient spectrum management. By utilizing movable antennas (MAs), ISCPT enhances reliability and flexibility, supporting various intelligent Internet of Things (IoT) scenarios. While joint optimization of parameters like beamforming and antenna positioning improves performance, it also introduces computational complexity that may affect efficiency. To enable flexible beamforming, we propose a novel deep reinforcement learning (DRL) architecture where heterogeneous agents independently adjust antenna configurations. This approach improves sensing accuracy, communication reliability, and power transfer efficiency, enhancing system capabilities and adaptability to dynamic environments. This work lays the foundation for wireless systems that integrate intelligent communication, sensing, and power transfer, offering improved performance in real-world applications.

查看原文本刊更多论文

可移动天线集成传感、通信和输电系统中基于学习的柔性波束形成方法

集成传感、通信和电力传输（ISCPT）系统对于下一代智能通信至关重要，能够实现高效的频谱管理。通过使用可移动天线（MAs）， ISCPT提高了可靠性和灵活性，支持各种智能物联网场景。虽然波束成形和天线定位等参数的联合优化提高了性能，但也引入了可能影响效率的计算复杂度。为了实现灵活的波束形成，我们提出了一种新的深度强化学习（DRL）架构，其中异构代理独立调整天线配置。该方法提高了传感精度、通信可靠性和功率传输效率，增强了系统的能力和对动态环境的适应性。这项工作为集成智能通信、传感和电力传输的无线系统奠定了基础，在实际应用中提供了更好的性能。

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

IEEE Communications Letters 工程技术-电信学

CiteScore

8.10

自引率

7.30%

发文量

590

审稿时长

2.8 months

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.