{"title":"基于强化学习的无人机辅助MIMO通信系统频谱性能优化","authors":"Lu Dong;Hong-Wei Kong;Xin Yuan","doi":"10.1109/JAS.2025.125225","DOIUrl":null,"url":null,"abstract":"Dear Editor, This letter is concerned with the problem of stable high-quality signal transmission of unmanned aerial vehicle (UAV)-assisted multiple-input multiple-output (MIMO) communication system. The particle swarm optimization (PSO) algorithm is used to achieve optimal beamforming and power allocation for this system. Additionally, sensitive particle (SP) and parameter adaptive adjustment are introduced into the traditional PSO algorithm, aiming to improve the performance of the PSO algorithm in dynamic environments with real-time changes in the UAV position. A reinforcement learning (RL)-based approach is proposed to obtain optimal UAV trajectory and adaptive adjustment strategy for PSO parameters, which combine with a specific obstacle avoidance scheme to achieve accurate UAV navigation while satisfying high-quality signal transmission. Simulation experiments show that our scheme provides higher and more stable spectral efficiency as well as more efficient UAV navigation than the currently commonly used scheme with a single RL approach.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 6","pages":"1283-1285"},"PeriodicalIF":19.2000,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11036657","citationCount":"0","resultStr":"{\"title\":\"Reinforcement Learning-Based Spectral Performance Optimization for UAV-Assisted MIMO Communication System\",\"authors\":\"Lu Dong;Hong-Wei Kong;Xin Yuan\",\"doi\":\"10.1109/JAS.2025.125225\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Dear Editor, This letter is concerned with the problem of stable high-quality signal transmission of unmanned aerial vehicle (UAV)-assisted multiple-input multiple-output (MIMO) communication system. The particle swarm optimization (PSO) algorithm is used to achieve optimal beamforming and power allocation for this system. Additionally, sensitive particle (SP) and parameter adaptive adjustment are introduced into the traditional PSO algorithm, aiming to improve the performance of the PSO algorithm in dynamic environments with real-time changes in the UAV position. A reinforcement learning (RL)-based approach is proposed to obtain optimal UAV trajectory and adaptive adjustment strategy for PSO parameters, which combine with a specific obstacle avoidance scheme to achieve accurate UAV navigation while satisfying high-quality signal transmission. Simulation experiments show that our scheme provides higher and more stable spectral efficiency as well as more efficient UAV navigation than the currently commonly used scheme with a single RL approach.\",\"PeriodicalId\":54230,\"journal\":{\"name\":\"Ieee-Caa Journal of Automatica Sinica\",\"volume\":\"12 6\",\"pages\":\"1283-1285\"},\"PeriodicalIF\":19.2000,\"publicationDate\":\"2025-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11036657\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ieee-Caa Journal of Automatica Sinica\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11036657/\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ieee-Caa Journal of Automatica Sinica","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11036657/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Reinforcement Learning-Based Spectral Performance Optimization for UAV-Assisted MIMO Communication System
Dear Editor, This letter is concerned with the problem of stable high-quality signal transmission of unmanned aerial vehicle (UAV)-assisted multiple-input multiple-output (MIMO) communication system. The particle swarm optimization (PSO) algorithm is used to achieve optimal beamforming and power allocation for this system. Additionally, sensitive particle (SP) and parameter adaptive adjustment are introduced into the traditional PSO algorithm, aiming to improve the performance of the PSO algorithm in dynamic environments with real-time changes in the UAV position. A reinforcement learning (RL)-based approach is proposed to obtain optimal UAV trajectory and adaptive adjustment strategy for PSO parameters, which combine with a specific obstacle avoidance scheme to achieve accurate UAV navigation while satisfying high-quality signal transmission. Simulation experiments show that our scheme provides higher and more stable spectral efficiency as well as more efficient UAV navigation than the currently commonly used scheme with a single RL approach.
期刊介绍:
The IEEE/CAA Journal of Automatica Sinica is a reputable journal that publishes high-quality papers in English on original theoretical/experimental research and development in the field of automation. The journal covers a wide range of topics including automatic control, artificial intelligence and intelligent control, systems theory and engineering, pattern recognition and intelligent systems, automation engineering and applications, information processing and information systems, network-based automation, robotics, sensing and measurement, and navigation, guidance, and control.
Additionally, the journal is abstracted/indexed in several prominent databases including SCIE (Science Citation Index Expanded), EI (Engineering Index), Inspec, Scopus, SCImago, DBLP, CNKI (China National Knowledge Infrastructure), CSCD (Chinese Science Citation Database), and IEEE Xplore.