# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 3 Related Works
### Conventional Handover Approaches
The results demonstrated that the FAHP-TOPSIS approach significantly reduced redundant handovers, minimized delays, enhanced resource utilization, and improved overall network performance compared to traditional techniques. However, the study did not consider real-world deployment challenges, such as dynamic network congestion and security considerations, which should be explored in future research. In 2017, [PERSON] and [PERSON] (2017) investigated an improved handover decision algorithm for heterogeneous wireless networks. The authors developed a multicriteria handover decision-making algorithm, which integrates LTE, WiMAX, and WLAN technologies and employs three distinct handover decision strategies: equal priority, mobile priority, and network priority. This approach was designed to improve handover precision, ensure better traffic distribution, and select the most suitable network based on multiple performance metrics beyond RSS. Furthermore, they assessed how different prioritization strategies influenced handover efficiency and overall network performance. The research was validated through simulations in a heterogeneous network setup, where various handover conditions were analyzed using different prioritization methods. Performance was measured based on handover probability, failure rates, and the efficiency of network resource utilization. The results revealed that all three proposed strategies outperformed conventional RSS-based methods, with the network-priority strategy demonstrating the best performance by reducing handover failures and enhancing load balance. However, the study did not consider real-world mobility fluctuations and security concerns, which should be explored in future studies. In 2021, [PERSON] et al. (2021) conducted a study on optimal handover decisions in mmWave cellular networks using a Markov Decision Process (MDP) model. The researchers proposed an MDP-based handover decision framework, designed to optimize cell selection by considering multiple factors such as dynamic channel load, link quality, and mobility patterns. The proposed model applies a Value Iteration Algorithm (VIA) to solve the MDP and determine optimal handover decisions, reducing unnecessary handovers and improving network stability. Additionally, they analyzed how different handover strategies, including load-based, rate-based, and channel-based approaches, perform under varying network conditions. The methodology involved simulations in a mmWave 5G environment, where network conditions, cell loads, and user mobility were modeled dynamically. Performance evaluation was conducted based on handover frequency, spectral efficiency, and signaling overhead. The results indicated that the MDP-based approach significantly reduced handover occurrences while improving network capacity and QoS for users. However, the study did not consider real-world deployment complexities, such as interference management, energy efficiency, and multi-cell coordination, which should be considered in future research. In 2015, [PERSON] et al. (2021) conducted research on a vertical handover decision algorithm using multicriteria metrics in heterogeneous wireless networks. The primary objective was to enhance handover efficiency by incorporating multiple decision factors rather than relying solely on received signal strength (RSS), addressing the problem of inefficient handovers, unbalanced network loads, and service disruptions caused by single-criterion methods. To tackle these challenges, the authors proposed a multicriteria vertical handover decision algorithm, which integrates three network interfaces, WLAN, WCDMA, and WiMAX, and evaluates handover decisions based on RSS, mobile speed, traffic class, and network occupancy. The approach included three prioritization strategies, equal priority, mobile priority, and network priority, ensuring that handover decisions align with different network conditions and user requirements.