无线电通信技术2024，Vol.50Issue(5)：914-920,7.DOI:10.3969/j.issn.1003-3114.2024.05.009
基于机器学习的太赫兹信道预测建模研究
Research on Terahertz Channel Prediction Modeling Based on Machine Learning
摘要
Abstract
The intricate communication scenarios in 6G mobile communication pose significant challenges,including high modeling complexity,prohibitive measurement costs,and overwhelming data volumes.Back Propagation Neural Network(BPNN)from machine learning is applied to indoor terahertz channel modeling to overcome these challenges.This approach effectively reduces modeling com-plexity and improves modeling efficiency.A BPNN channel parameter prediction model based on a hybrid optimization of Genetic Algo-rithm(GA)and Ant Colony Optimization(ACO)is established to study and predict large-and small-scale characteristics of terahertz wireless channels.Prediction results are compared with traditional BPNN model,GA-BP,and ACO-BP,and the accuracy and effective-ness of the established model are verified.Results indicate that the error between the predicted and actual values of Genetic Algorithm-Ant Colony Optimization-Back Propagation(GA-ACO-BP)model is smaller and a better fit.The model demonstrated superior prediction performance compared to other three models.BPNN based on GA-ACO hybrid optimization can learn and predict channel pa-rameters with a small amount of data,making it applicable for future measurement-based wireless channel modeling analysis.关键词
太赫兹/信道建模/射线跟踪/机器学习Key words
terahertz/channel modeling/ray tracing/machine learning分类
电子信息工程引用本文复制引用
王世豪,李双德,刘芫健,梁静宜,蒋晨晨..基于机器学习的太赫兹信道预测建模研究[J].无线电通信技术,2024,50(5):914-920,7.基金项目
国家自然科学基金(62371248) （62371248）
南京邮电大学引进人才自然科学研究启动基金(NY222059) National Natural Science Foundation of China(62371248) （NY222059）
Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(NY222059) （NY222059）
