Spectrum Efficiency Optimization of Multi-UAV Communication Network Based on Cooperative Spectrum Sensing

被引：0
|
作者：
Zhang H. [1 ]
Da X. [2 ]
Hu H. [2 ]
Ni L. [3 ]
Pan Y. [1 ]
机构：
[1] Graduate School, Air Force Engineering University, Xi'an
[2] Information and Navigation College, Air Force Engineering University, Xi'an
[3] The Unit 95263 of PLA, Xiaogan
来源：
Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology | 2021年 / 41卷 / 08期
关键词：
Cognitive radio; Convex optimization; Spectrum efficiency; Spectrum sensing; Unmanned air vehicle;
D O I：
10.15918/j.tbit1001-0645.2020.122
中图分类号：
学科分类号：
摘要：
Aiming at the low spectrum efficiency (SE) in application scenarios of unmanned air vehicles (UAV), an average SE optimization scheme of multi-UAV communication network combined with cognitive radio (CR) was proposed. Firstly, based on cooperative spectrum sensing (CSS), a cognitive UAV network model was established for multi-UAV cooperation in air to ground (A2G) channel, setting the optimization parameters such as number of UAVs, sensing time and cooperative decision threshold. Then, the average SE optimization problem of UAV was investigated, and a joint optimization algorithm was proposed to solve the non-convex optimization problem. Finally, the change of SE in the flight course of UAV was analyzed. The simulation results show that there is an optimal sensing time to maximize the SE, and the number of UAVs and decision threshold will affect the optimal value of SE. In addition, the proposed algorithm has better convergence and can effectively improve the average SE of UAVs in the secondary network. © 2021, Editorial Department of Transaction of Beijing Institute of Technology. All right reserved.
引用
收藏
页码：830 / 839
页数：9
相关论文
共 25 条
  • [1] ORFANUS D, FREITAS E P D, ELIASSEN F., Self-organization as a supporting paradigm for military UAV relay networks, IEEE Communications Letters, 20, 4, pp. 804-807, (2016)
  • [2] NIU H, GONZALEZ P N, HEATH R W., A UAV-based traffic monitoring system-invited paper, Proceedings of 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), pp. 1-5, (2018)
  • [3] HAYAJNEH A M, ZAIDI S A R, MCLERNON D C, Et al., Performance analysis of UAV enabled disaster recovery networks: a stochastic geometric framework based on cluster processes, IEEE Access, 6, pp. 26215-26230, (2018)
  • [4] ZHANG Shuhang, ZHANG Hongliang, BOYA D I, Et al., Cellular UAV-to-X communications: design and optimization for multi-UAV networks in 5G, IEEE Transactions on Wireless Communications, 18, 2, pp. 1346-1359, (2019)
  • [5] ZHAO Nan, LU Weidang, SHENG Min, Et al., UAV-assisted emergency networks in disasters, IEEE Wireless Communications, 26, 1, pp. 45-51, (2019)
  • [6] SANTANA G M D, CRISTO R S, DEZAN C, Et al., Cognitive radio for UAV communications: opportunities and future challenges, Proceedings of 2018 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 760-768, (2018)
  • [7] JACOB P, SIRIGINA R P, MADHUKUMAR A S, Et al., Cognitive radio for aeronautical communications: a survey, IEEE Access, 4, pp. 3417-3443, (2016)
  • [8] YAO Weiqing, YI Benshun, A novel encoding and decoding method of LT codes and application to cognitive radio, Journal of Electronics & Information Technology, 41, 3, pp. 571-579, (2019)
  • [9] ZHANG Hongwei, DA Xinyu, HU Hang, Multi-UAV cooperative spectrum sensing in cognitive UAV network, Proceedings of 2019 International Conference on Communication and Information Processing (ICCIP), pp. 273-278, (2019)
  • [10] LI He, OTA K, DONG Mianxiong, Learning IoT in edge: deep learning for the internet of things with edge computing, IEEE Network, 32, 1, pp. 96-101, (2018)
123