Learning-based user association and dynamic resource allocation in multi-connectivity enabled unmanned aerial vehicle networks

Abstract

Unmanned Aerial Vehicles (UAVs) as aerial base stations to provide communication services for ground users is a flexible and cost-effective paradigm in B5G. Besides, dynamic resource allocation and multi-connectivity can be adopted to further harness the potentials of UAVs in improving communication capacity, in such situations such that the interference among users becomes a pivotal disincentive requiring effective solutions. To this end, we investigate the Joint UAV-User Association, Channel Allocation, and transmission Power Control (J-UACAPC) problem in a multi-connectivity-enabled UAV network with constrained backhaul links, where each UAV can determine the reusable channels and transmission power to serve the selected ground users. The goal was to mitigate co-channel interference while maximizing long-term system utility. The problem was modeled as a cooperative stochastic game with hybrid discrete-continuous action space. A Multi-Agent Hybrid Deep Reinforcement Learning (MAHDRL) algorithm was proposed to address this problem. Extensive simulation results demonstrated the effectiveness of the proposed algorithm and showed that it has a higher system utility than the baseline methods.

Keywords

• UAV-user association
• Multi-connectivity
• Resource allocation
• Power control
• Multi-agent deep reinforcement learning