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

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

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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.

UAV-user associationMulti-connectivityResource allocationPower controlMulti-agent deep reinforcement learning

Zhipeng Cheng、Minghui Liwang、Ning Chen、Lianfen Huang、Nadra Guizani、Xiaojiang Du

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Department of Information and Communication Engineering,Xiamen University,Xiamen,361005,China

School of Electrical and Computer Engineering,University of Texas at Arlington,Arlington,TX,76019,USA

Department of Electrical and Computer Engineering,Stevens Institute of Technology,Hoboken,NJ,07030,USA

国家自然科学基金国家自然科学基金国家自然科学基金Digital Fujian Province Key Laboratory of IoT Communication,Architecture and Safety Technology国家自然科学基金重点项目福建省自然科学基金

6197136561871339621713922010499617310122021J01004

2024

10.1016/j.dcan.2022.05.026

数字通信与网络(英文)

数字通信与网络(英文)

ISSN：
年,卷(期)：2024.10(1)
  • 39