# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 3 Related Works
### AI-Based Approaches  
Recent advancements in artificial intelligence have led to the development of intelligent handover mechanisms tailored for next-generation wireless networks. This subsection examines a range of AI-based approaches, highlighting the use of machine learning, deep learning, and reinforcement learning to enable adaptive, data-driven decision-making in dynamic mobility scenarios. In 2021, [PERSON] et al. presented a comprehensive review [50] of how machine learning techniques have been applied to optimize HO decisions in 5G and future networks. The authors classify existing approaches into supervised, unsupervised, and reinforcement learning methods, and analyze them based on performance metrics such as handover failure rate, ping-pong rate, and latency. Emphasis is given to RL-based techniques, such as Q-learning, due to their adaptability in dynamic mobile environments. The survey highlights the growing complexity of HO management in ultra-dense and heterogeneous networks, where traditional rule-based algorithms fall short. However, the paper notes several limitations: most reviewed models are validated only in simulation environments, with little real-world deployment; many approaches lack energy-awareness, scalability, or security considerations; and few models address the joint optimization of multiple conflicting objectives. Moreover, interpretability remains a challenge, especially for deep learning-based models. The study concludes that while ML offers significant promise, there is a pressing need for lightweight, secure, and context-aware solutions that can operate efficiently in real time and under resource constraints. The paper provides a valuable foundation but stops short of proposing a unified or hybrid framework. In 2022, [PERSON] and [PERSON] presented a state-of-the-art review [39] of AI techniques applied to optimize HO processes in advanced wireless systems. The authors focus on the limitations of conventional handover mechanisms in highly dynamic environments and highlight how AI can improve decision-making under mobility, dense connectivity, and multi-tier architectures. The authors emphasize RL as a promising paradigm due to its ability to learn from interaction and adapt to real-time network conditions. Despite their promise, most AI approaches face deployment challenges, including computational overhead, training data availability, lack of interpretability, and absence of standardization across heterogeneous infrastructures. Additionally, the paper points out that many studies neglect energy efficiency, security, and cross-layer optimization. The survey concludes by proposing future research directions such as lightweight AI models, privacy-preserving training, and integration with edge computing and blockchain. While comprehensive, the review does not include empirical performance evaluations or practical deployment case studies. The 2024 study by [PERSON] et al. [51] proposed a predictive handover strategy tailored for 6G networks, integrating deep and transfer learning within the O-RAN architecture. The research addresses mobility management challenges caused by smaller cells and higher signal attenuation in dense deployments. The authors formulate handover prediction as a multi-class classification task using sequential UE measurements (RSRP and SINR) across time windows. Their deep learning model, comprising an encoder, stacked LSTM layers, and a decoder, is designed to predict future serving cells with high accuracy while minimizing training time in network conditions. Evaluation on a simulated urban macro-cell network showed a 92% prediction accuracy, outperforming baseline ML models like Random Forest, MLP, and GRU. The framework also achieved a 91% and 77% reduction in retraining time using transfer learning when adapting to new handover parameters or UAV deployments, respectively. A key strength is its compliance with O-RAN specifications, allowing deployment as an xApp in near real-time RICs.