# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
## 2 Related Work  
Improper cell selection decisions can lead to several issues that can affect the overall network services and, therefore, negatively impact the user experience. Recent studies have investigated HO problems in V2X communication and suggested solutions. This section provides an overview of recent studies that consider machine learning for cell selection strategies, highlighting their contributions and limitations. This section concludes the paper with a brief outline of the proposed approach. A hybrid self-organizing network (SON) model is adopted in [22]. A SON is a type of network management approach that can adjust network responses to user requirements using automated processes to improve network responsiveness, efficiency, and capacity. The SON architecture was defined by the 3 GPP LTE Advanced Standards for centralized, distributed, and hybrid implementations [23]. The SON architecture focuses on self-configuration, self-optimization, and self-healing. The central SON transfers the estimated optimal handover parameters to the distributed SON to improve the learning process efficiency. The selection of the actions is based on increasing or decreasing the value of two parameters, namely, Time-To-Trigger (TTT) and Cell Individual Offset (CIO), aiming to enhance the overall network performance by reducing the number of link failures and ping-pong HOs. However, prior knowledge stored in centralized databases can become irrelevant or outdated due to the frequent changes in the V2X network topology, which can ultimately lead to suboptimal decisions. In [24], the study followed a centralized RL execution agent approach for base station (BS) selection decisions. They designed an ML-Based HO algorithm for Vehicle-to-Network (V2N) using a real data set collected from the city of Glasgow, United Kingdom, facilitating UE location and trajectory. By using the relative information of the BS Reference Signal Received Power (RSRP) collected from the E-UTRAN measurement report, the reward function normalizes the target RSRP value from both the current selected BS and the local maximum RSRP. This approach enabled the agent to choose the strongest possible signal in the environment, leading to a stable connection. Although the proposed algorithm significantly outperforms the 3 GPP RSRP method, it requires reducing the state space and gathering additional relevant network information to improve the HO decision. The Deep Q-Learning (DQN) handover optimization method was employed in [25] for a UDN to predict the handover parameters/threshold while considering the signal fading conditions. Signal fading is a common wireless phenomenon that significantly affects the overall network performance as a result of the variation in signal strength when it travels through the medium. High path loss, signal propagation, Doppler shifting, and shadowing are the primary sources of signal fading. The LSTM-assisted digital twin is leveraged to optimize handover parameters, thereby enhancing system efficiency and convergent effects, and making the handover process more robust for dynamic network environments. The reward function is designed to enhance handover rate effectiveness, aiming to maintain seamless connectivity by reducing link failures and minimizing ping-pong HOs. The simulation results demonstrate that the enhanced DQN with a digital twin outperforms the baseline DQN by achieving an effective handover rate of 2.7%. The provided module is not involved in the handover decision and is delegated to the existing handover control system. Despite the extensive literature on mobility load balancing in cellular networks, the amount of research within the scope of vehicular communications is limited. The UE counters connection failures when the cell cannot accommodate more traffic. RL was adopted to enhance the efficacy of the load balancing algorithm [26]. The vehicular environment provides rewards as a prompt response to the UE for the actions/behavior in a particular state.