计算机技术与发展2025，Vol.35Issue(7)：48-54,7.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0036
基于联邦学习的卫星系统通感波束成形方法
Federated Learning Based ISAC Beamforming Method for Satellite Systems
摘要
Abstract
Satellite communication plays a crucial role in the development of 6G and is an important way to achieve seamless global con-nectivity and provide diversified services.Among them,Integrated Sensing and Communication(ISAC),as one of the core technologies of 6G,can achieve the integration of communication and perception,improve spectrum utilization,and maximize communication and perception performance in resource limited satellite systems.We propose an optimization problem for maximizing the communication signal-to-noise ratio of a satellite system.By solving the optimal solution of this optimization problem,a solution scheme for producing beamforming matrices from channel matrices is constructed.Specifically,based on the obtained federated learning dataset,we innovatively propose an optimization solution for the integrated beamforming matrix of satellite system sensing.Considering the training process of federated learning,the parameters for model training include factors such as channel matrix,beamforming matrix,and power al-location coefficient.The training of the model is deployed to the user side for execution,while the satellite side serves as the server-side for optimizing the aggregation of the model.The simulation results show that compared with commonly used integrated sensing beamforming schemes,the beamforming scheme using federated learning can bring better integrated sensing performance.Therefore,we propose a federated learning based integrated beamforming method for satellite system sensing,which simplifies the calculation of the downlink sensing integrated beamforming matrix based on optimization problems,and effectively improves the sensing integration performance of satellite systems with good universality.关键词
卫星通信/通感一体化/波束成形/优化问题/联邦学习Key words
satellite communication/integrated of sensing and communication/beamforming/optimization problem/federated learning分类
信息技术与安全科学引用本文复制引用
赖海光,朱邦兵,沈金海,万坤,王泽渝..基于联邦学习的卫星系统通感波束成形方法[J].计算机技术与发展,2025,35(7):48-54,7.基金项目
江苏省重点研发计划(BE2022080) （BE2022080）
