UAV Deployment for Optimal Mobility Management and Connectivity in Heterogeneous Environments

Improving mobility management and connectivity in fifth-generation (5G) and beyond 5G mobile networks, particularly in heterogeneous (HetNet) environments, poses a significant challenge due to performance degradation and high spectrum consumption resulting from increased base stations (BS) and device density. Traditional resource allocation (RA) methods for enhancing network coverage, such as genetic algorithms (GA) and particle swarm optimization (PSO), are often computationally expensive and ineffective due to their high computational requirements and slow convergence in dense network scenarios. This paper introduces a novel RA architecture based on a deep Q-network (DQN) explicitly tailored for UAV deployment within HetNets, considering interference restrictions. The impact of UAV altitude and quantity on network performance is evaluated through a mixed-integer nonlinear program designed to optimize these variables. Compared to existing AI methods, our approach enhances connectivity and significantly improves network throughput and management efficiency. Our findings demonstrate a considerable advancement over traditional optimization techniques, promoting more efficient and scalable UAV integration into HetNet-based environments.