A Similarity-Based Method for Base Station Selection in 5G Networks

Mobile devices face the problem of handover signal sources in mobile communication networks. It is particularly difficult to address this problem in 5G networks, because the coverage area of 5G base stations is smaller than that of traditional mobile communication networks, e.g., 4G networks. Therefore, when a device is moving, it needs to frequently handover base stations to maintain the connection with the network in the 5G network. Given that 5G networks have high requirements for low latency and high reliability, new methods need to be proposed to optimize the selection of gNBs for devices. The prediction of the UEs’ trajectory can reduce redundant handovers between devices and base stations. However, the prediction of the UEs’ movement trajectory requires sufficient user movement data to achieve the desired prediction accuracy. For users with less trajectory data, inaccurate predictions may cause negative effects and even increase the number of handovers. In this paper, we propose a gNB handover model based on user similarity. The evaluation results show that our method can reduce the number of handovers by 50% compared with existing related solutions, which means that the proposed method will effectively reduce the transmission delay and enhance the robustness of the gNB.