# A Novel Base-Station Selection Strategy for Cellular Vehicle-to-Everything (C-V2X) Communications
## 1 Introduction  
Cellular vehicle-to-everything (C-V2X) wireless communication technology is a new generation of communication technology that connects vehicles to everything. Compared with traditional vehicle-to-vehicle communications, C-V2X could leverage the comprehensive coverage of secure and well-established networks. It could also support highly reliable, real-time communication services at high speeds and in high-density traffic. Ultimately, C-V2X will play a pivotal role in developing automated-driving technology [1]. C-V2X acquires information based on wireless cellular technology, and it analyzes the collected information to obtain correct instructions. Wireless cellular technology utilizes cellular base station relays for data transmission between vehicles or data centers and among vehicles over a wide area. Normally, there are two kinds of base stations for wireless cellular technology. Macrocell base stationshave large transmission power and wide coverage and can meet a wide range of user requirements. Macrocell base station signals are, however, usually blocked by buildings during vehicle movement, even in the case of traffic jams and density due to a large numbers of users; as a result, the macrocell signal is greatly interfered. Therefore, setting up a femtocell base station in the blind spot of a macrocell base station can improve signal transmission quality in a specific area. The distance between the vehicle and the base station is inversely proportional to the RSS, so the RSS obtained from the femtocell base station is more stable and higher. However, the femtocell base station has a lower transmit power, so coverage is limited. With the cooperation of two kinds of base stations, a macro-femtocell system can provide stable signal transmission services to C-V2X. When the vehicle is in motion, we need to select the appropriate base station to provide an efficient transmission service. In previous work, a reinforcement learning-based Markov decision policy for base station selection was studied in [2]. The differences between the previous work and this paper are as follows. Firstly, this paper considers that femtocell base stations have a usage limit. Secondly, the previous work did not consider the random mobility of vehicles, while we predict the position of the vehicles' next action. Finally, in this system, each vehicle's action is independent, and the choices made by each vehicle affect the communication quality of the system. We need to have an optimal base station selection strategy according to the Markov decision policy. When choosing a base station, we face the following problems:  
1. For automatic vehicles, there are often more than one feasible path variables in a complex environment due to the randomness of automatic movement. It is therefore difficult to predict a user's next position. 2. With inefficient base station selection, when a femtocell base station is overloaded, unnecessary interference and signal interruption are generated. To address the above problems, we propose a method to formulate base station selection as a Markov decision problem. The contributions of our work can be summarized as follows:  
1. Predict the RSS from two kinds of base stations based on the current state of the vehicle. 2. Use the Markov decision policy to predict all base station selection possibility sequences and obtain the most appropriate selection sequence. In this paper, we mainly consider the prediction of multi-vehicle base station selection. If combined with the analysis of the existing state of the vehicle, according to our proposed base station selection strategy, it can also be used for the prejudgment of a handover option with each vehicle in the system. The remainder of this paper is organized as follows. In Section 2, we introduce the related work. In Section 3, background and preliminaries are presented. In Section 4, we propose a Markov decision policy mechanism for multiple vehicles. In Section 5, we put forward a method to predict RSS.