# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
## 2 Related Work
The reward mechanism in this study was designed to incentivize the UE to balance the load between the two BSs (the current and neighboring BSs) to maintain spectral efficiency. The UE receives a reward based on the current BS load status and the CIO value. When the neighboring cells receive sufficient load, the UE receives a reward. The CIO value decreases to zero. An insufficient load causes the CIO to increase, transferring the UE from the overloaded cell and penalizing the UE. Although the mechanism demonstrates efficient load distribution between cells, it should consider the density of the vehicular network by incorporating multiple-cell scenarios. Another limitation is that limited contextual information was used without considering the mobility of the UE. To enhance HO decisions, a mobility-adaptive handover mechanism was developed in [27], which reduces reliance on the handover static parameter, thereby minimizing service interruptions caused by unnecessary HOs for high-speed users. The intelligent mobility management method (LIM2) utilizes the RSS-prediction technique to identify the future highest signal quality for the target cell. Subsequently, the State-Action-Reward-State-Action (SARSA) algorithm, an online reinforcement learning method, was employed to dynamically identify the optimal cell for the triggered handover. To improve the signal quality and power, the Reference Signal Received Quality (RSRQ) of the neighbor cell served as the reward function. However, the results indicated an improvement in the overall throughput and lower HO failure compared to the baseline mechanisms; neglecting the load balancing can be a constraint limiting the network handover performance. A novel reinforcement learning-based approach, named Advanced Conditional Handover (ACHO), is investigated in [28], aiming to improve the mobility robustness in 5G and future cellular networks. Conditional Handover (CHO) is a 3 GPP feature designed to reduce handover failures by pre-configuring handover parameters for the UE. However, CHO still experiences failures due to non-optimal handover parameters, leading to radio link failure (RLF), and the inappropriate TTT and Handover Margin (HOM) values lead to premature or delayed handovers. As 5G networks become denser, the frequency of these problems is expected to increase. In ACHO, epsilon-greedy Q-Learning is used to dynamically optimize handover decisions based on real-time network conditions, user speed, and previous handover results. ACHO allows the UE to determine its own handover parameters (HOM and TTT) based on the current network conditions, historical handover performance, user service requirements (e.g., eMBB and uRLLC), and mobility speed. Overall, the method enhances CHO by giving the UE more autonomy in making handover decisions, unlike traditional CHO, which relies on static configurations from base stations. This approach exhibits an inadequate solution for dense networks characterized by high-mobility users with a dynamic network nature, owing to the use of a single parameter, causing more unnecessary handovers. Table 2 presents a comparative analysis of RL-based approaches for HO optimization. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref. **} & \multirow{2}{*}{**Ref. **} & \multirow{2}{*}{**Ref. **} & \multirow{2}{*}{**Ref. **} & \multirow{2}{*}{**Ref. **} & \multirow{2}{*}{**Result/** Performance**} & \multirow{2}{*}{**Key** Contributions**} & \multirow{2}{*}{**Limitations**} \\  & & & & & & \\ \hline \multirow{4}{*}{ \begin{tabular}{c} [22] \\ IEEE \\ 2021 \\ \end{tabular}  } & \multirow{4}{*}{**Ref. **} & \multirow{4}{*}{**Ref. **} & \multirow{4}{*}{**Ref. **} & Adopted twenty-four times faster and improved the user satisfaction rate by & Proposed ML based on a mobility robustness optimization & The UDN paradigm is not considered, and infrequently updated centralized databases \\  & & & 417.7\% more than the non-ML algorithm.