# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
## 3 Intelligent Handover Decision for V2X (IHD-V2X)  
The fast-paced developments of 5G V2X networks have posed new problems in sustaining seamless connectivity caused by high UE mobility, high SC density, and varied application requirements. These problems result in frequent handovers, unnecessary network signaling, increased packet loss, latency, and wasteful resource utilization. Handling the dynamic network conditions caused by these problems renders the traditional HO mechanism insufficient, necessitating a change to the use of an adaptive and intelligent handover management approach. In this paper, we propose a novel HO approach that integrates Q-Learning with the MCDM TOPSIS technique to optimize the overall 5G V2X network performance metrics. The proposed intelligent distributed method works well for latency-sensitive applications such as AR [29]. TOPSIS offers a strong MCDM method for ranking SCs based on several parameters, guaranteeing that the network choice of possible handover targets is well-informed. The handover decision-making process is then optimized by Q-Learning using this ranked list to learn and adapt over time based on \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**RL Algorithm** & **Strengths** & **Weaknesses** \\ \hline Q-Learning & A model-free algorithm to identify the optimal policy. It is considered a trial-and-error-free algorithm. The epsilon-greedy policy is applied to balance selection selection. & \(\bullet\) Scalability Problems: Because the Q-table expands exponentially with the number of states and actions, Q-Learning has trouble handling vast or continuous state and action spaces. \\ \hline Deep Q-Learning & Combines the Artificial Neural Network (ANN) with Q-Learning and is suitable for making a decision in a complex environment. & \(\bullet\) Complexity: DQN is more difficult to implement and calls for a large increase in processing and memory capacity. DQN converges more slowly since a neural network must be trained, particularly in situations with sparse input. \\  & & \(\bullet\) Sample inefficiency: DQN frequently needs a lot of training data to function well, which can be difficult in settings where data collection is costly or takes a long time. DQN may experience stability problems during training, such as fluctuating policies or divergent Q-values. \\ \hline Distributed Reinforcement & The training process is accelerated due to the decoupling of the tasks of acting and learning. & \(\bullet\) Coordination complexity: This can increase the coordination complexity between different agents, resulting in slowing down the learning process. \\ Learning & and learning. & \(\bullet\) Overhead communications: This can lead to low network efficiency due to large amounts of information sharing, which in turn increases communications costs. \\  & & \(\bullet\) Communication complexity: This can result in intensive resource utilization. \\ \hline State–Action– Reward– State–Action (SARSA) & Simplifying the implementation does not require modeling the environment. & \(\bullet\) Suboptimal decision: The on-policy learning algorithm follows the current policy, as it depends on the current behavior of the agent, which can limit the potential to explore better actions. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Strengths and weaknesses of RL algorithms. accumulated experience and rewards. Considering a variety of criteria, the combination of MCDM with Q-Learning enables the network to make more complex judgments and learn from past mistakes to make better decisions in the future. This method offers other benefits, including  
* Decreased exploration space for Q-Learning: The IHD-V2X algorithm efficiently narrows down the search space for Q-Learning by pre-filtering and ranking the SCs using TOPSIS rather than considering every potential small cell. Q-Learning concentrates on the top-ranked possibilities that TOPSIS has found.