Multi-feature fusion for high-speed railway propagation scene recognition based on LSTM networks

In order to fulfill the requirement of service quality of the 5G system, we consider multi-feature fusion methods in this paper to recognize the propagation scene of high-speed railway precisely. After that, the performance of system is improved by using some adaptive technologies. Firstly, we make an explanation on propagation scenes and channel feature parameters, and the dataset is split into two sets: training set and testing set. Then we propose a multilayer long short term memory(LSTM) architecture with a novel weighted score fusion scheme to learn classification from different propagation scenes and compare the results with usage of three regular fusion schemes. The result shows that the recognition accuracy of proposed model on testing dataset reaches 92.2%, the area under curve(AUC) of this model is better than the other three fusion schemes. Therefore, the proposed method provides an accurate recognition application for high-speed railway communication systems.