基于多机协作的认知无人机网络能效联合优化

doi:10.7527/S1000-6893.2020.24548

电子电气工程与控制

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基于多机协作的认知无人机网络能效联合优化

张宏伟¹, 达新宇², 胡航³, 倪磊¹, 潘钰¹

1. 空军工程大学研究生院, 西安 710077;
2. 阳光学院人工智能学院, 福州 350015;
3. 空军工程大学信息与导航学院, 西安 710077

收稿日期:2020-07-16 修回日期:2020-08-28 出版日期:2021-06-15 发布日期:1900-01-01
通讯作者: 胡航 E-mail:xd_huhang@126.com
基金资助:
国家自然科学基金（61901509）；博士后创新人才计划（BX201700108）；空军工程大学校长基金（XZJK2019033）；空军工程大学信息与导航学院创新基金（YNLX1904025）

Energy-efficient cooperative optimization for multi-UAV-aided cognitive radio networks

ZHANG Hongwei¹, DA Xinyu², HU Hang³, NI Lei¹, PAN Yu¹

1. Graduate School, Air Force Engineering University, Xi'an 710077, China;
2. College of Artificial Intelligence, Yango University, Fuzhou 350015, China;
3. Information and Navigation College, Air Force Engineering University, Xi'an 710077, China

Received:2020-07-16 Revised:2020-08-28 Online:2021-06-15 Published:1900-01-01
Supported by:
National Natural Science Foundation of China (61901509); National Postdoctoral Program for Innovative Talents (BX201700108); The President Foundation of Air Force Engineering University (XZJK2019033); The Innovation Foundation of Air Force Engineering University (YNLX1904025)

摘要/Abstract

摘要： 针对无人机（UAV）通信网络中频谱资源紧缺的问题，构建基于认知无线电的多无人机通信网络，通过多机协作频谱感知有效探索授权频谱。提出一种基于Bisection算法的迭代算法，通过联合优化感知时间和判决门限对构建的复杂非凸问题求解，显著提高了无人机次级认知网络的能量效率（EE）。分析了无人机飞行过程中能效的变化情况，仿真结果表明，存在最优感知时间使能效获得最大值，且判决门限的选择会影响该能效最优值；提出的高能效迭代算法具有较好收敛性，有效提高了认知无人机网络的能量利用率。

关键词: 协作频谱感知, 无人机(UAV), 认知无线电, 能量效率(EE), 凸优化

Abstract: Aiming at the shortage of spectrum resources in Unmanned Air Vehicle (UAV) communication networks, we construct a multi-UAV communication network model based on cognitive radio, and explore the authorized spectrum effectively through cooperative spectrum sensing. An iterative algorithm based on the bisection algorithm is proposed, and the Energy Efficiency (EE) of UAV secondary cognitive networks is significantly improved by jointly optimizing the sensing time and decision threshold to solve the complex nonconvex problem. Finally, the change of EE in the flight course of UAVs is analyzed. The simulation results show that there is an optimal sensing time to maximize the EE, and that the selection of the decision threshold will affect the optimal value of the EE; with good convergence, the proposed EE iterative algorithm effectively improves the energy utilization of cognitive UAV networks.

Key words: cooperative spectrum sensing, Unmanned Air Vehicles (UAV), cognitive radio, Energy Efficiency (EE), convex optimization

中图分类号:

V243.1

张宏伟, 达新宇, 胡航, 倪磊, 潘钰. 基于多机协作的认知无人机网络能效联合优化[J]. 航空学报, 2021, 42(6): 324548-324548.

ZHANG Hongwei, DA Xinyu, HU Hang, NI Lei, PAN Yu. Energy-efficient cooperative optimization for multi-UAV-aided cognitive radio networks[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(6): 324548-324548.

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E-mail：hkxb@buaa.edu.cn

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基于多机协作的认知无人机网络能效联合优化

Energy-efficient cooperative optimization for multi-UAV-aided cognitive radio networks

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