基于蜂窝网络的多无人机能量消耗最优化算法研究

doi:10.11959/j.issn.1000-436x.2023025

Abstract

Abstract:

In complex time-varying environment, the ground base station (GBS) may not assist the UAV.Therefore, a mobile edge computing (MEC) cellular-connected network based on digital twin (DT) technology was studied.Given the efficiency of multi-UAV, multiple high-altitude balloon (HAB) equipped with MEC servers were introduced.On this basis, an energy minimization problem for all UAV was proposed, and a multi-UAV trajectory optimization and resource allocation scheme was presented to solve it.The double deep Q-network (DDQN) was applied to handle the association between multi-UAV and multi-HAB, and the multi-UAV trajectory and computing resource allocation were jointly optimized by the successive convex approximation (SCA) and the block coordinate descent (BCD).Simulation experiments verify the feasibility and effectiveness of the proposed algorithm.The system energy consumption is reduced by 30%, better than the comparison algorithms.

Key words: UAV, task unloading, digital twins, DDQN, SCA

CLC Number:

TN92

Jingming XIA, Yufeng LIU, Ling TAN. Research on multi-UAV energy consumption optimization algorithm for cellular-connected network[J]. Journal on Communications, 2023, 44(2): 185-197.

Figures/Tables 12

References ［24］

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参数	含义
K	无人机数量
M	配备MEC服务器的高空气球的数量
x_k［n］	在时隙n，无人机k水平位置的横坐标
y_k［n］	在时隙n，无人机k水平位置的纵坐标
H_k	在时隙n，无人机k的飞行高度
x_m	高空气球m水平位置的横坐标
y_m	高空气球m水平位置的纵坐标
H_m	高空气球m的悬停高度
β₀	信道功率增益
v_max	无人机的最大飞行速度
L_{k ,I}	无人机k初始位置
L_{k ,F}	无人机k终止位置
d_min	无人机间的最小安全距离
B	系统带宽
D_k［n］	在时隙n，无人机k产生的计算任务大小
T_k［n］	在时隙n，无人机k完成计算任务的最大时延

参数	预设值
K	2
M	2
β₀/dB	-30
v_max/(m·s^-1)	30
B/MHz	50
?/(rad·s^-1)	400
r/m	0.5
U_tip	?r
P _i/W	79.07
V₀/(m·s^-1)	3.6
ξ	0.000 1
d₀	0.075

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Research on multi-UAV energy consumption optimization algorithm for cellular-connected network

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