Research on Terahertz Channel Prediction Modeling Based on Machine Learning
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.
国家科技期刊平台
NSTL
万方数据
