通感算协同的无人机群轨迹规划与功率分配

doi:10.19665/j.issn1001-2400.2023.03.006

Abstract

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

CLC Number:

TN929.5

WU Yihao,QI Yanli,ZHOU Yiqing,CAI Qing,LIU Ling,SHI Jinglin. UAVs trajectory planning and power allocation based on the convergence of communication,sensing and computing[J].Journal of Xidian University, 2023, 50(3): 61-74.

Figures/Tables 11

References 37

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参数设置		参数值
系统总带宽/MHz		10^[36]
带宽分辨率/kHz		200
无人机数量/架		1/3
救援人员数量/人		200/600
救援人员移动速度/(m·s^-1)		1^[33]
单架无人机覆盖范围/m		半径为100的圆^[35]
无人机飞行高度/m		200^[37]
无人机飞行速度/(m·s^-1)		10^[37]
救援人员活动范围(受灾区域面积)/m		半径为200/400的圆
	Volte业务	51 kb/s通信速率	10 ms时延
救援人员业务类型	eMBB业务	100 Mb/s通信速率	10 ms时延
	uRLLC业务	10 Mb/s通信速率	1 ms时延
网络效用阈值/%		90
救援人员服务满意度阈值/%		95

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

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References 37

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