西安电子科技大学学报 ›› 2023, Vol. 50 ›› Issue (3): 61-74.doi: 10.19665/j.issn1001-2400.2023.03.006

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通感算协同的无人机群轨迹规划与功率分配

吴义豪1,2(),齐彦丽1,2(),周一青1,2,3(),蔡青1,2(),刘玲1,2(),石晶林1,2,3()   

  1. 1.中国科学院大学,北京 100049
    2.中国科学院计算技术研究所 处理器芯片全国重点实验室,北京 100190
    3.中国科学院计算技术研究所 移动计算与新型终端北京市重点实验室,北京 100190
  • 收稿日期:2022-12-16 出版日期:2023-06-20 发布日期:2023-10-13
  • 通讯作者: 齐彦丽
  • 作者简介:吴义豪(2000—),男,中国科学院大学博士研究生,E-mail:wuyihao22z@ict.ac.cn;|周一青(1975—),女,研究员,E-mail:zhouyiqing@ict.ac.cn;|蔡 青(1997—),女,中国科学院大学博士研究生,E-mail:caiqing19s@ict.ac.cn;|刘 玲(1990—),女,副研究员,E-mail:liuling@ict.ac.cn;|石晶林(1972—),男,研究员,E-mail:sjl@ict.ac.cn
  • 基金资助:
    国家重点研发计划(2021YFB2900203)

UAVs trajectory planning and power allocation based on the convergence of communication,sensing and computing

WU Yihao1,2(),QI Yanli1,2(),ZHOU Yiqing1,2,3(),CAI Qing1,2(),LIU Ling1,2(),SHI Jinglin1,2,3()   

  1. 1. University of Chinese Academy of Sciences,Beijing 100049,China
    2. Beijing Key Laboratory of Mobile Computing and Pervasive Device,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China
    3. State Key Lab of Processors,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China
  • Received:2022-12-16 Online:2023-06-20 Published:2023-10-13
  • Contact: Yanli QI

摘要:

区域性自然灾害会造成地面基础通信设施的损坏,无人机群组网可作为空中基站恢复通信。现有研究集中于静态场景下如何在无人机频谱和电池容量受限的条件下为救援人员提供高效通信服务。然而,实际场景中救援人员的位置移动和业务变化会导致静态方案失效。针对这一问题,提出了通感算协同的无人机群调度算法。首先实时感知环境信息,即救援人员历史位置信息和业务需求,并对救援人员未来位置和业务需求进行预测,为无人机群的调度提供先验信息;其次,针对无人机负载约束提出了改进的k-sums算法用于实现无人机群位置的部署,以实现无人机群负载均衡;最后,进一步采用强化学习算法,对无人机群的发射功率进行优化,在有限带宽下保证救援人员的通信服务质量。仿真结果表明,相比于静态场景下基于信噪比建立救援人员与无人机群关联,所提的无人机群调度算法能够有效提升约20%的网络效用(网络通信收益与通信成本之差),为应急救灾场景下救援人员的业务服务提供保障。

关键词: 通感算协同, 无人机, 应急通信, 强化学习

Abstract:

Regional natural disasters often cause damage to ground-based communication facilities,and UAVs networks can act as aerial base stations to restore communications.Existing research has focused on how to provide efficient communication services to rescuers in static scenarios with a limited UAV spectrum and battery capacity.However,the location movement and service changes of communication rescuers in real scenarios lead to the failure of static schemes.To solve this problem,this paper proposes a collaborative UAVs scheduling algorithm through the convergence of communication,sensing and computing.First,we perform sensing the environmental information,i.e.,the rescuers' historical location information and service demand,in real-time to realize the prediction of the rescuers' future location and service demand and provide a priori information for the scheduling of UAVs.Second,an improved k-sums algorithm is proposed to deploy the UAVs' location concerning the UAV load constraint to achieve UAVs' load balancing.Furthermore,a reinforcement learning algorithm is used to optimize the UAVs' transmit power to ensure rescuers' communication service quality under a limited bandwidth.Compared to static scenarios where rescuer-UAV associations are established based on signal-to-noise ratios,the proposed UAV scheduling algorithm through the convergence of communication,sensing and computing in this paper can effectively improve network utility (network communication benefits minus communication costs) by 20%.The algorithm provides a guaranteed business experience for rescuers in emergency disaster relief scenarios.

Key words: convergence of communication, sensing and computing, unmanned aerial vehicles, emergency communications, reinforcement learning

中图分类号: 

  • TN929.5
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