# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
## 3 Intelligent Handover Decision for V2X (IHD-V2X)
Because it works with a more focused collection of superior choices, the Q-Learning process can converge more quickly and effectively because of this reduction in the search space. * Enhanced performance and adaptability: High-quality SCs selection is made possible by the TOPSIS approach, which also incorporates Q-Learning for adaptive learning and handover strategy refinement, based on feedback and real-world network performance. With the help of this adaptive learning feature, the network may continuously improve the handover procedure in response to shifting circumstances, including traffic loads, user movement patterns, and signal conditions, thereby improving resilience and performance. * Balancing long-term optimization and immediate quality: TOPSIS prioritizes instant quality using performance measurements to rank SCs. By drawing lessons from the past and refining future handover procedures to maximize cumulative rewards, Q-Learning presents a long-term view. Collectively, they guarantee that the network makes choices that promote long-term network stability and efficiency, in addition to optimizing the quality of handovers. * Assistance in complex and dynamic environments: This method is especially well-suited for complex and dynamic environments such as 5G V2X networks, where quick decision-making and adaptive learning are essential, because it integrates TOPSIS and Q-Learning. TOPSIS guarantees sound decision-making in multi-criteria scenarios, whereas Q-Learning manages the dynamic nature of the environment by determining the best course of action under various circumstances. Figure 1 illustrates the key Q-Learning components, where a state is mapped to a sequence of actions while interacting with an environment to maximize rewards. The knowledge gained from the environment by the agent is represented by a Q-table (Q). It keeps track of the predicted utility, or Q-values, of performing particular tasks under particular conditions. Figure 1: Q-Learning for V2X communications. The following parameters are considered for SCs' performance evaluation: RSSI: It represents a traditional HO decision method that provides a measure of how good a received signal is to a UE. SINR: It is a user-perceived metric that evaluates the ratio of signal level to noise level. A high SINR value represents good signal quality, while a value below 0 indicates more noise, which means low connection speed and a high probability of losing connection. BER: It is one of the most used performance indicators in wireless networks. It is calculated by dividing the erroneous bits by the number of transmitted bits. It is used in decision-making in cognitive radio networks to determine channel quality. This parameter has been used to analyze the performance and achievable throughput of 5G uplink and downlink communications, and as a reference value for the vertical handover algorithm. Data Transmission Rate: It represents the amount of data that is transmitted. Maintaining seamless and uninterrupted service is essential for upholding user satisfaction, and this is ensured by higher data rates. By choosing smaller cells with faster data rates, the network efficiency can be maximized by transferring data faster and freeing up resources for other users. Packet loss: It represents the dropped packets when the data is transmitted over a network. Delay: It is a crucial component of safety-related communications in V2X scenarios, as even a slight delay might have serious repercussions. It calculates the amount of time the data takes to transfer from source to destination. The following discusses the flow of the algorithm. 1. **Define the State (\(s_{v}\))** A state (\(s_{v}\)) represents the connection context currently loaded by the UE according to the application requirements. The state consists of network parameters such as the Received Signal Strength Indicator (RSSI), Signal-to-Interference-Noise Ratio (SINR), Bit Error Rate (BER), data rate, delay, and packet loss. With each movement, a UE in the network changes its state. 2.