## 4 Related Works
Future advancements in handover management are expected to leverage edge computing, blockchain-based authentication, and self-organizing networks to further enhance the efficiency of smart city infrastructures. These insights contribute to the ongoing development of next-generation wireless networks, ensuring high reliability and seamless connectivity in urban environments. [PERSON] et al. [21] investigate the interplay between handover management and load balancing in UDNs, emphasizing their significance in enhancing network performance and user experience. Handover mechanisms are crucial in maintaining seamless connectivity, especially in high-mobility environments, where frequent transitions between small cells can lead to increased latency and connection failures. This study explores predictive, AI-driven, and context-aware handover techniques to minimize disruptions and optimize resource allocation. In addition, load balancing is examined as a key factor in ensuring a fair distribution of traffic across network nodes, preventing congestion, and improving overall system efficiency. This paper highlights dynamic load balancing approaches, including ML algorithms and SDN, to adaptively allocate resources in real time. Future research directions emphasize the integration of these strategies with edge computing and blockchain-based authentication to enhance network resilience and scalability. These insights contribute to the development of next-generation wireless systems with improved reliability and efficiency. [PERSON] et al. [22] present a comprehensive survey of ML-based load balancing algorithms designed for next-generation heterogeneous networks (HetNets), emphasizing their role in managing user traffic across densely deployed macro- and small cells. The paper categorizes classical and AI-driven strategies such as cell range expansion, cell breathing, and interference mitigation and evaluates supervised, unsupervised, and reinforcement learning techniques for dynamic traffic redistribution. Notably, reinforcement learning and deep reinforcement learning emerge as promising tools due to their adaptability and scalability in complex network environments. The authors analyze over 30 models, benchmarking them against key performance indicators like SINR, throughput, and call drop rate. This work provides valuable insight into the design and deployment of intelligent, self-optimizing load balancing frameworks essential for sustaining service quality in future 5G and 6G cellular networks. [PERSON] and [PERSON] [59] propose an intelligent UAV-based device-to-device communication model tailored for future 5G/6G cellular networks, combining deep learning optimization techniques with swarm intelligence and clustering algorithms. Their framework integrates Improved Hybrid Particle Swarm Optimization with K-means (IHPSO-K), Hybrid Fuzzy C-Means (HFCM), and a greedy selection mechanism to enhance UAV placement and cluster formation under varying network demands. By adopting both device-centric- and network-centric strategies, the model maximizes either throughput or the number of served devices while addressing constraints such as backhaul capacity, bandwidth, and SINR. Simulation results using MATLAB (v25.1.0.2943329) and a multi-UAV testbed validate the system's superior performance in energy efficiency, scalability, and link reliability. This work demonstrates the effectiveness of AI-optimized UAV deployments for robust and adaptive D2D communication in ultra-dense 5G/6G environments. [PERSON] et al. [60] provides an in-depth analysis of load balancing self-optimization (LBSO) within 5G mobile networks. This study highlights the significance of load balancing due to the increasing number of users and the growing use of small cell sizes in 5G cellular networks. The authors develop a simulation model to investigate the performance of LBSO under various mobile speed scenarios, focusing on optimizing handover control parameters (HCPs) to balance loads between cells.