Unmanned aerial vehicles (UAVs) play a crucial role in enhancing network coverage and capacity, especially in areas with high user density or limited infrastructure. This paper proposes an effective UAV-assisted offloading framework to minimize the energy consumption of both users and UAVs in an air-to-ground (A2G) network. First, UAVs are deployed by jointly considering the user distribution and guaranteeing the quality of service (QoS) of users. Further, user association, power control, and bandwidth allocation are jointly optimized, aiming to minimize the power consumption of users. Considering user mobility, the positions of UAVs are continuously refined using the double deep Q-network (DDQN) algorithm to reduce the weighted energy consumption of users and UAVs. Simulation results show that the proposed algorithm has better performance in reducing the total energy consumption compared with benchmark schemes.

This paper investigates handover in hybrid visible light communication (VLC)/radio frequency (RF) networks. In such a network, mobile users are prone to experience frequent handovers (FHOs). To this end, we propose a collaborative online learning-based handover scheme (COLH) in hybrid VLC/RF 5G systems. By selecting the next access point (AP) to which a user should handover, our goal is to make the user–AP connection as long as possible after the handover, defined as a reward that is learned online through a multi-armed bandit (MAB) framework. Unlike previous schemes based on independent and collective learning, first, our scheme dynamically clusters users with similar feedback on a given AP. Second, the users in the same cluster collaborate in estimating the expected reward for that AP, and the one with the maximum expected reward is selected as the next AP. This scheme can be implemented without extensive offline training and location information; thus, its practicality is greatly enhanced. The simulation results show that the proposal outperforms existing benchmarks on reducing handovers.