# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 3 Related Works
### Conventional Handover Approaches
This research [3] aims to overcome the limitations of conventional handover algorithms, which struggle with frequent handover failures, ping-pong effects, and latency issues, especially under high-speed and ultra-dense deployment scenarios. Moreover, the study addresses the lack of adaptive models that simultaneously consider multiple radio parameters such as RSRP and SINR during decision-making. The model dynamically adjusts offset values for RSRP and SINR to enable intelligent handover decisions based on real-time network measurements. The study was conducted through simulation in Python 3.12, modeling a dense 5G mmWave network with 50 gNBs and 500 pieces of UE, operating at 26 GHz and a mobility speed of 200 km/h. The proposed model improved the handover success rate by up to 15%, reduced latency by approximately 6 s, and decreased packet loss ratio by 1.3% compared to the LIM2 model. It also maintained higher adaptability under varying SINR and speed conditions while achieving efficient real-time learning and target cell selection. However, this study does not evaluate computational overhead or convergence time of the reinforcement learning component. Additionally, real-world deployment factors, such as backhaul delay, energy consumption, and multi-user interference, were not modeled, which may affect the scalability and feasibility of the proposed algorithm in practical 5G and 6G systems. In 2023, [PERSON] et al. conducted a study [49] focused on analyzing handover performance in ultra-dense heterogeneous networks with traffic hotspots. The authors recognized that conventional handover analysis models--based on Poisson point processes (PPPs)--fail to capture the spatial coupling between base stations (BSs) and users in real-world traffic hotspots. Additionally, earlier studies often overlooked critical aspects like sojourn time and edge effects, which impact the reliability of handover decisions. To overcome these shortcomings, this research proposed a unified handover analysis framework using Poisson cluster processes (PCPs) to model the location of small cells in hotspot regions and introduced a Modified Random Waypoint (MRWP) mobility model to mitigate density wave issues inherent in traditional mobility models. Closed-form analytical expressions were derived for key handover metrics, including handover rate, handover failure rate, and ping-pong rate, considering parameters such as BS density, scattering variance, UE velocity, and handover triggering thresholds. The authors analytically modeled handover events under the PCP-based deployment using stochastic geometry and validated their formulas through extensive simulations in a 5 km \(\times\) 5 km network region. They considered three-tier network topologies with macro- and small cells, adopting realistic 3 GPP path-loss and power models. The performance was assessed through mathematical derivations and verified using Monte Carlo simulations. Nevertheless, the research did not explore the computational complexity or runtime feasibility of the proposed MRWP model. Moreover, real-world implementation factors such as signal measurement inaccuracies, backhaul latency, and energy consumption were not included, which may limit the applicability of the model in operational networks.