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 ...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.展开更多
空中无人机基站因具有高灵活性等优势,可扩大对地面用户的通信支持范围,在战场环境下可以解决地面机动用户的移动覆盖问题。考虑到战场环境中高层地形特征对无线电信号的影响、用户差异化的通信需求,以及无人机基站能耗问题,对地面用户...空中无人机基站因具有高灵活性等优势,可扩大对地面用户的通信支持范围,在战场环境下可以解决地面机动用户的移动覆盖问题。考虑到战场环境中高层地形特征对无线电信号的影响、用户差异化的通信需求,以及无人机基站能耗问题,对地面用户信道容量、无人机能耗、无人机基站动态部署分别建模,提出了一种基于异构移动用户的强化学习通信覆盖算法(Reinforcement Learning Communication Coverage Algorithm Based on Heterogeneous Mobile Users,ABS-RL),旨在为地面用户提供高质量的通信服务。仿真结果表明,该算法在提高地面用户信道容量、降低无人机基站总能耗方面有显著优势。展开更多
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)+1 种基金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.
文摘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.
文摘空中无人机基站因具有高灵活性等优势,可扩大对地面用户的通信支持范围,在战场环境下可以解决地面机动用户的移动覆盖问题。考虑到战场环境中高层地形特征对无线电信号的影响、用户差异化的通信需求,以及无人机基站能耗问题,对地面用户信道容量、无人机能耗、无人机基站动态部署分别建模,提出了一种基于异构移动用户的强化学习通信覆盖算法(Reinforcement Learning Communication Coverage Algorithm Based on Heterogeneous Mobile Users,ABS-RL),旨在为地面用户提供高质量的通信服务。仿真结果表明,该算法在提高地面用户信道容量、降低无人机基站总能耗方面有显著优势。
