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 Rein-forcement 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.
基金项目
国家自然科学基金(61971365)
国家自然科学基金(61871339)
国家自然科学基金(62171392)
Digital Fujian Province Key Laboratory of IoT Communication,Architecture and Safety Technology(2010499)
国家自然科学基金重点项目(61731012)
福建省自然科学基金(2021J01004)
NETL
NSTL
万方数据