## 4 Related Works
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Key Focus** & **Challenges Addressed** & **Future Research Directions** \\ \hline \multirow{4}{*}{[PERSON] et al. [22]} & Survey of ML-based load balancing strategies in HetNets & Managing user traffic in dense networks, inefficiency of classical methods, benchmarking RL/DRL against KPIs (SINR, throughput) & Development of scalable RL/DRL frameworks, integration of real-time analytics, and federated learning for load distribution \\ \hline \multirow{4}{*}{[PERSON] and [PERSON] [59]} & AI-driven UAV-based D2D communication model for 5G/6G & UAV placement optimization, SINR/bandwidth/backhaul constraints, scalable clustering hybrid UAV-ground coordination under dynamic network loads. \\ \hline \multirow{4}{*}{[PERSON] et al. [60]} & Load balancing self-optimization using HCP tuning in 5G & PPHP, RLF, spectral inefficiency at varying speeds; ineffective static HCP & Context-aware HCP optimization; fuzzy and distance-based models for adaptive mobility-aware load balancing. \\ \hline \multirow{4}{*}{[PERSON] et al. [23]} & Roadmap for UAV cellular communications from 5G to 6G & Cell selection, interference, coverage limitations in UAV networks & Non-terrestrial networks, cell-free and THz systems, and AI-driven UAV networking architectures. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._including interference management, resource allocation, and energy consumption. Various methodologies, such as advanced beamforming techniques, ML-driven optimization, and interference coordination strategies, are analyzed to mitigate these challenges. In addition, the paper discusses the role of small-cell networks in enabling seamless connectivity and network densification, emphasizing their adaptability to diverse deployment scenarios. Emerging technologies, including self-organizing networks and hybrid architectures, are also explored to enhance the scalability and flexibility of UDNs. The findings contribute to the ongoing research on optimizing ultra-dense small-cell deployments for next-generation wireless communication systems. The work of [PERSON]. [19] outlines key future research directions in the evolution of UDNs and small-cell deployments. This research emphasizes the need for advanced interference management techniques to enhance network performance in increasingly congested environments. Future studies should explore intelligent resource allocation strategies, leveraging artificial intelligence and ML to optimize spectrum efficiency. Additionally, energy-efficient small-cell architectures are highlighted as a critical research area in relation to efforts to minimize power consumption while maintaining high network capacity. The paper also discusses the potential integration of UDNs with emerging technologies such as 6G, edge computing, and block chain for enhanced security and connectivity. Moreover, self-organizing networks and dynamic reconfiguration mechanisms are proposed to improve network adaptability. These future directions aim to address the scalability, efficiency, and sustainability challenges of next-generation wireless networks, paving the way for more robust and intelligent small-cell deployment. [PERSON] et al. [20] explore the critical role of handover management in ensuring seamless connectivity within UDNs, particularly in smart city environments. Efficient handover mechanisms are essential to maintain service continuity, minimize latency, and optimize network resources. The study highlights various handover strategies, including predictive and AI-driven models, to enhance mobility management and reduce handover failures. In addition, it discusses the integration of small cells in smart cities, enabling improved coverage, energy efficiency, and scalability. This paper emphasizes how intelligent transportation systems, IoT devices, and urban automation rely on robust handover protocols to support real-time applications.