Temporal-Spatial Collaborative Prediction for LTE-R Communication Quality Based on Deep Learning

In recent years, long term evolution for railway (LTE-R) has been a promising technology to meet the growing demand for railway wireless communication. To realize the active maintenance of LTE-R base station, it is of great significance to precisely predict the communication quality (CQ) of LTE-R base station. Given that the existing LTE CQ prediction methods can not support the active maintenance of LTE-R base station. Furthermore, the LTE-R base station has its unique characteristics in time relationship and regional impact, one of the most challenging problems is to effectively integrate the temporal and spatial information to improve the effect of CQ prediction. To solve the above problems, we choose daily evolved radio access bearer (E-RAB) abnormal release ratio as the CQ indicator, and propose a new deep learning-based CQ prediction approach for LTE-R. Considering the influence of adjacent base stations, this method conducts temporal-spatial collaborative prediction on multivariate time series collected from the CQ data of these stations. First, to eliminate the negative effect of redundant variables, a new variable filter method based on max-relevance, and min-redundancy (MRMR) criterion and binary particle swarm optimization (BPSO) is proposed to select a variable set from the CQ data of related base stations. Second, a new recurrent convolutional neural network (RCNN) model with a self-attention mechanism is proposed to extract temporal-spatial features from the selected variable set. With these features, we build a collaborative prediction model for CQ prediction. Experimental results on real-world LTE-R CQ datasets demonstrate the superiority of the proposed method in CQ prediction.

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