Learning-based user association and dynamic resource allocation in multi-connectivity enabled unmanned aerial vehicle networks
Zhipeng Cheng, Minghui Liwang, Ning Chen, Lianfen Huang, Nadra Guizani, Xiaojiang Du
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.