Deep Learning based Location Prediction with Multiple Features in Communication Network

With the development of wireless communication technologies and explosive increases number of UEs, data traffic grows rapidly so that much denser deployment is essential. More frequent handover cause higher latency and throughput reduction, which has a negative impact on the network performance and users’ satisfaction. For the applications of 5G network including resource allocation and mobility management, it is essential to predict the positions of mobile users in the future so as to make preparation in advance. In this paper, we propose a Long Short-term Memory (LSTM) model based location prediction considering the wireless measurement reports from the serving base station and the neighbour base stations, and introduce orientation loss function in order to enable the model to acknowledge the information on the direction of the UE movement. Extensive numerical experiments demonstrated that the proposed LSTM model based on multiple features and orientation information can achieve better performance on the location prediction.