Deep-Learning Based Scenario Identification for High-Speed Railway Propagation Channels

Propagation scenario identification is of vital significance for boosting the performance of future smart high-speed railway (HSR) communication networks. This paper investigates the HSR propagation scenario identification model, based on deep learning networks and feature fusion methods. With the assist of railway long-term evolution (LTE) networks, the channel impulse responses are collected in four typical HSR scenarios including unobstructed viaduct, obstructed viaduct, station and suburban. Four channel characteristics involving power delay profile, root mean square (RMS) delay spread, RMS angular spread and Rice K-factor form the datasets used for model training and testing. Then, a novel propagation scenario identification model is proposed by merging a weighted score based feature fusion method into the long short-term memory (LSTM) neural network. The hyperparameters such as time window length and numbers of hidden units and layers are determined by autocorrelation analysis and cross-validation. Finally, the model performance is evaluated by focusing on the impact of different feature fusion methods and computational complexity.