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

作　　者：Zhipeng Cheng Minghui Liwang Ning Chen Lianfen Huang Nadra Guizani Xiaojiang Du 

机构地区：[1]Department of Information and Communication Engineering,Xiamen University,Xiamen,361005,China [2]School of Electrical and Computer Engineering,University of Texas at Arlington,Arlington,TX,76019,USA [3]Department of Electrical and Computer Engineering,Stevens Institute of Technology,Hoboken,NJ,07030,USA

出　　处：《Digital Communications and Networks》2024年第1期53-62,共10页数字通信与网络（英文版）

基　　金：supported in part by the National Natural Science Foundation of China(grant nos.61971365,61871339,62171392);Digital Fujian Province Key Laboratory of IoT Communication,Architecture and Safety Technology(grant no.2010499);the State Key Program of the National Natural Science Foundation of China(grant no.61731012);the Natural Science Foundation of Fujian Province of China No.2021J01004.

摘　　要：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.

关 键 词：UAV-user association Multi-connectivity Resource allocation Power control Multi-agent deep reinforcement learning 

分 类 号：V279[航空宇航科学与技术—飞行器设计] TN92[电子电信—通信与信息系统]