# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 3 Related Works
### Conventional Handover Approaches  
This subsection presents an overview of conventional handover decision-making approaches that employ structured reasoning and multicriteria evaluation techniques. Specifically, it analyzes studies utilizing fuzzy logic, AHP, and TOPSIS to improve handover accuracy and adaptability in dynamic network environments. In 2022, [PERSON], [PERSON] et al. [31] worked on the development of adaptive handover decision using fuzzy logic for 5G ultra-dense networks. This study aimed to address the drawbacks of conventional handover schemes, which are too simple to adapt to the diverse handover scenarios encountered in 5G UDNs and consider only UE signal strength. Also, the paper aims to cover the lack of investigation for various handover decision algorithms with various mobility speed scenarios in the 5G network. This study proposed an FL-based handover scheme to dynamically adjust the values of two handover parameters, namely HOM and TTT, concerning each UE. The proposed scheme, abbreviated as FLDHDT, has dynamic adjustment of TTT in addition to HOM by using the signal-to-interference-plus-noise ratio and horizontal moving speed of the UE as inputs to the FL controller. The work has been performed by simulations, which were performed on ns-3 by using the conventional Event A3, FLDH, and FLDHDT handover schemes. To perform evaluations, the 5G UDN topology was used. The work comes out with the Taiwan Ministry of Science and Technology. However, this work did not consider base station load balancing, which could further optimize handover decisions. Also, real-world deployment constraints, such as hardware processing limitations, were not analyzed. In 2017, the study by [PERSON], [PERSON], [PERSON], and [PERSON] [42] focused on multi-attribute vertical handover decision-making in a hybrid VLC-Femto system. The researchers proposed an analytic hierarchy process-cooperative game (AHP-CG)-based vertical handover (VHO) algorithm, designed to evaluate handover decisions based on multiple criteria, including traffic type, network load, and link quality. The AHP was used to determine the relative importance of each criterion, while the cooperative game (CG) model compensated for the AHP's limitations by effectively comparing decision criteria values. In addition, they analyzed how different handover strategies (immediate handover, delayed handover, and AHP-based approaches) performed under various network conditions. The methodology involved simulations conducted in a home environment with hybrid VLC and femtocell access points, modeling dynamic user mobility and network load. Performance was measured using handover success rate, packet transfer delay, packet loss ratio, and number of unnecessary handovers. The study found that the proposed AHP-CG algorithm outperformed conventional approaches by reducing unnecessary handovers, optimizing network selection, and maintaining a lower packet loss ratio while adapting to different traffic demands. However, it did not consider real-world implementation challenges, such as mobility prediction and energy efficiency, which should be explored in future work. In 2021, a research study conducted by [PERSON] and [PERSON] (2021) explored a multicriteria-based vertical handover decision-making algorithm for heterogeneous networks. The authors introduced a two-phase vertical handover decision model incorporating a hybrid fuzzy analytic hierarchy process (FAHP) and the technique for order preference by similarity to ideal solution (TOPSIS). This approach first assesses the necessity of handover and then selects the most appropriate network based on multiple factors, such as QoS parameters, handover cost, and energy efficiency. Furthermore, they evaluated the effectiveness of their method by benchmarking it against existing algorithms, including single-criterion, AHP-based, and TOPSIS-based models. The study was carried out using simulations in MATLAB R2021a, modeling a heterogeneous wireless network environment with varying mobility patterns and network conditions.