光通信技术2025，Vol.49Issue(3)：16-21,6.DOI:10.13921/j.cnki.issn1002-5561.2025.03.003
改善FSO信道估计的机器学习研究进展
Progress in machine learning research for improving FSO channel estimation
摘要
Abstract
To address the nonlinear channel issues caused by atmospheric turbulence and scattering effects in free space optical(FSO)communication systems and enhance the transmission reliability of optical communication systems in the sixth generation mobile communication(6G)era,this paper reviews the research progress of machine learning(ML)in improving FSO channel estimation.It compares and analyzes the applications of deep learning and non-deep learning methods in FSO channel estimation,demonstrating the advantages of ML in enhancing estimation accuracy and system performance.Finally,the challenges and future development trends of ML in FSO communication are discussed,highlighting the significant potential of ML algorithms in FSO systems and exploring future research directions.关键词
自由空间光通信/机器学习/大气衰减/信道估计/研究进展Key words
free space optical communication/machine learning/atmospheric attenuation/channel estimation/research progress分类
电子信息工程引用本文复制引用
张颜鹭,王安蓉,邵宇丰,朱耀东,柳海楠,陈超,胡文光,李文臣..改善FSO信道估计的机器学习研究进展[J].光通信技术,2025,49(3):16-21,6.基金项目
国家自然科学基金项目(61107064)资助 （61107064）
重庆市教委科学技术研究计划重大资助项目(KJZD-M201901201)资助 （KJZD-M201901201）
重庆市三峡库区地质环境监测与灾害预警重点实验室开放基金重大资助项目(ZD2020-A0104)资助 （ZD2020-A0104）
浙江省重点研发计划资助项目(2017C01043)资助. （2017C01043）
