Wireless Channel Scenario Classification Based on Semi-supervised Learning
To address the issue of poor generalization of supervised learning,which can only effectively classify which channel scenario the channel data used for training belongs to,this paper proposes a wireless channel scenario classification method based on pseudo-label semi-supervised learning.Simulation results indicate that,when classifying the channel scenario corresponding to new data(originating from different sources but belonging to a known category of channel scenario in the model),the semi-supervised learning approach significantly outperforms supervised learning in terms of classification accuracy.Thus it can be seen,it is concluded that semi-supervised learning can enhance the generalization ability of wireless channel scenario classification models.
国家科技期刊平台
NSTL
万方数据
维普
