无人机空地安全通信与航迹规划的多目标联合优化方法

doi:10.13229/j.cnki.jdxbgxb20220557

Abstract

Abstract:

Aiming at the problem of UAV secrecy communication and ensuring safety as well energy saving during flight in the wireless network scenario， a multi-objective optimization scheme was proposed. The scheme mainly includes UAV transmission model， UAV energy consumption model and environmental constraints model， and further constructs the multi-objective optimization model of UAV scheduling and path optimization problem （USPOP）， which optimizes average communication secrecy rate of UAV wireless communication， UAV hovering energy consumption and UAV flight energy consumption. Then， a non-dominated sorting genetic algorithm III （NSGA-III） with discrete normal distribution initialization， differential mechanism， genetic mechanism and avoiding obstacles operator （NDGA-NSGA-III） is proposed to solve USPOP. Simulation results show that the proposed algorithm can effectively solve the constructed optimization problem， and the convergence effect is better than other comparison algorithms.

Key words: computer application, unmanned aerial vehicle（UAV） communication network, trajectory planning, secrecy rate, energy consumption, multi-objective optimization

CLC Number:

TP393

Ying HE,Jun-song FAN,Wei WANG,Geng SUN,Yan-heng LIU. Joint optimization of secure communication and trajectory planning in unmanned aerial vehicle air⁃to⁃ground[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 913-922.

Figures/Tables 10

Fig.1

Fig.2

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Fig.5

Table 1

Numerical results of average secrecy ratemaximization strategy obtained by different algorithms"

算法	$f 1$ /［bit·（s·Hz）^-1］	$f 2$ /kJ	$f 3$ /kJ
NDGA-NSGA-III	9.17	96.79	9 996.43
NSGA-III	0.75	1352.32	9 660.04
MOMVO	1.19	781.57	13 503.28
PESA2	0.76	1297.87	12 088.44
SPEA2	0.70	1477.13	10 071.86

Table 1

Table 2

Numerical results of hovering energy consumption minimization strategy obtained by different algorithms"

算法	$f 1$ ［bit·（s·Hz）^-1］	$f 2$ /kJ	$f 3$ /kJ
NDGA-NSGA-III	8.24	89.59	8 926.63
NSGA-III	0.19	883.39	10 320.59
MOMVO	0.16	453.58	13 944.13
PESA2	0.76	1 296.87	12 078.51
SPEA2	0.39	1 270.88	10 716.25

Table 2

Table 3

Numerical results of flight energy consumption minimization strategy obtained by different algorithms"

算法	$f 1$ /［bit·（s·Hz）^-1］	$f 2$ /kJ	$f 3$ /kJ
NDGA-NSGA-III	8.24	98.02	8 414.29
NSGA-III	0.0001	2 825.53	8 829.82
MOMVO	0.28	599.11	13 121.44
PESA2	0.76	1 314.87	12 059.52
SPEA2	0.58	1 542.76	9 911.77

Table 3

Fig.6

Fig.7

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