Multi-step Prediction of LTE-R Communication Quality based on CA-TCN and Differential Evolution

With the continuous development of heavy-haul railway technology, the demand for the faster transmission and higher bandwidth capacity of wireless communication network is also increasing. Therefore, Long Term Evolution for Railway (LTE-R) begin to replace Global System for Mobile Communications for Railway (GSM-R) to burden the core wireless communication services. In order to improve the operation and maintenance efficiency of LTE-R network, this paper proposes a multi-step LTE-R communication quality prediction method based on Differential Evolution algorithm and Temporal Convolutional Network with Coordinate Attention (TCNCA). Firstly, this method uses differential evolution and permutation importance index to filter the features of multivariate LTE-R communication quality data. Then, the coordinate attention mechanism and TCN network were fused to build the prediction model. Finally, this method still used Differential Evolution to adjust the model parameters based on Mann—Kendall test, so that the model could take into account the prediction accuracy and the trend of data change, thus realize the multi-step prediction for LTE-R communication quality. Experimental results on real data set show that the proposed method can provide decision support for the active maintenance of LTE-R network, and has high application value.