To solve the problems of unbalanced network loads, poor network throughput, and low user transmission rate in 5th generation (5G) vehicular networks, a user-centric data communication service (UCDCS) strategy is proposed for 5G vehicular networks. First, the UCDCS model is established. The access roadside unit group (ARSUG) is updated in real-time according to predictions of vehicle mobility. Then, a data communication service resource allocation algorithm based on game theory that considers the network load cost, throughput cost, and vehicle benefit is developed. Based on the results of the algorithm, roadside unit (RSU) selection based on entropy weight is performed and the best RSU for data communication is selected according to the different service preferences of each vehicle. This improves the transmission rate for users and realizes network load balance to ensure the quality of service for users. The simulation results show that the transmission rate of the UCDCS strategy is 35.16%, 23.46%, and 47.74% higher than those of the traditional handover management (THOM), improved mobility management (IMM), and network-centric network selection strategies (NCNS), respectively; correspondingly, the link reliability is at least 0.07% higher, and the network delay is at least 5.96% smaller for the UCDCS strategy.
INTRODUCTIONIn recent years, with the rapid development of intelligent applications in vehicular network scenarios such as intelligent travel, advanced driving, and remote driving, the need for corresponding business services and networks has rapidly arisen to meet the requirements of high bandwidth, low delay, and effective connection at any time and place [1]. The 5G network provides communication with high reliability, low delay, and large bandwidth [2][3][4][5][6]. The application of 5G technologies, such as ultradense network (UDN) and mobile edge computing (MEC), in vehicular networks' results in efficient real-time data communication among vehicles, roadside networks, and people andThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.