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。
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.
[1] ORFANUS D, DE FREITAS E P, ELIASSEN F. Self-organization as a supporting paradigm for military UAV relay networks[J]. IEEE Communications Letters, 2016, 20(4):804-807.
[2] 陈志勇. 面向无人机通信的频谱资源利用与优化[D]. 杭州:浙江理工大学, 2019:3-5. CHEN Z Y. Spectrum resource utilization and optimization for UAV communication[D]. Hangzhou:Zhejiang Sci-Tech University, 2019:3-5(in Chinese).
[3] SANTANA G M D, CRISTO R S, DEZAN C, et al. Cognitive radio for UAV communications:Opportunities and future challenges[C]//2018 International Conference on Unmanned Aircraft Systems (ICUAS), 2018:760-768.
[4] JACOB P, SIRIGINA R P, MADHUKUMAR A S, et al. Cognitive radio for aeronautical communications:A survey[J]. IEEE Access, 2016, 4:3417-3443.
[5] 刘海涛, 顾新宇, 方晓钰, 等. 频率选择性衰落信道DS-CDMA无人机中继通信系统航迹规划[J]. 航空学报, 2019, 40(7):322633. LIU H T, GU X Y, FANG X Y, et al. Path panning for UAV relay communication systems with DS-CDMA over frequency selective fading channel[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(7):322633(in Chinese).
[6] XU W B, WANG S, YAN S, et al. An efficient wideband spectrum sensing algorithm for unmanned aerial vehicle communication networks[J]. IEEE Internet of Things Journal, 2019, 6(2):1768-1780.
[7] ZHANG H W, DA X Y, HU H. Multi-UAV cooperative spectrum sensing in cognitive UAV network[C]//2019 International Conference on Communication and Information Processing (ICCIP), 2019:273-278.
[8] GHAZZAI H, BEN GHORBEL M, KADRI A, et al. Energy efficient management of unmanned aerial vehicles for underlay cognitive radio systems[J]. IEEE Transactions on Green Communications and Networking, 2017, 1(4):434-443.
[9] ZHANG J W, ZENG Y, ZHANG R. Spectrum and energy efficiency maximization in UAV-enabled mobile relaying[C]//2017 IEEE International Conference on Communications (ICC). Piscataway:IEEE Press, 2017:1-6.
[10] 杨明, 李翔, 杨昊, 等. 能量效率认知无线电协作感知和传输联合优化[J]. 西安电子科技大学学报, 2017, 44(3):101-107. YANG M, LI X, YANG H, et al. Joint optimization of cooperative sensing and transmission in energy-efficiency cognitive radio[J]. Journal of Xidian University, 2017, 44(3):101-107(in Chinese).
[11] PAN Y, DA X Y, HU H, et al. Energy-efficiency optimization of UAV-based cognitive radio system[J]. IEEE Access, 2019, 7:155381-155391.
[12] GHORBEL M B, GHAZZAI H, KADRI A, et al. An energy efficient overlay cognitive radio approach in UAV-based communication[C]//2018 IEEE Global Communications Conference (GLOBECOM). Piscataway:IEEE Press, 2018:1-6.
[13] SBOUI L, GHAZZAI H, REZKI Z, et al. Energy-efficient power allocation for UAV cognitive radio systems[C]//2017 IEEE 86th Vehicular Technology Conference (VTC). Piscataway:IEEE Press, 2017:1-5.
[14] HU H, DA X Y, HUANG Y C, et al. SE and EE optimization for cognitive UAV network based on location information[J]. IEEE Access, 2019, 7:162115-162126.
[15] SBOUI L, GHAZZAI H, REZKI Z, et al. Achievable rates of UAV-relayed cooperative cognitive radio MIMO systems[J]. IEEE Access, 2017, 5:5190-5204.
[16] MOZAFFARI M, SAAD W, BENNIS M, et al. Drone small cells in the clouds:Design, deployment and performance analysis[C]//2015 IEEE Global Communications Conference (GLOBECOM). Piscataway:IEEE Press, 2015:1-6.
[17] LIU L, ZHANG S W, ZHANG R. CoMP in the sky:UAV placement and movement optimization for multi-user communications[J]. IEEE Transactions on Communications, 2019, 67(8):5645-5658.
[18] HU H, DA X Y, NI L, et al. Green energy powered cognitive sensor network with cooperative sensing[J]. IEEE Access, 2019, 7:17354-17364.
[19] HULENS D, VERBEKE J, GOEDEME T. How to choose the best embedded processing platform for onboard UAV image processing[C]//2015 International Joint Conference Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), 2015.
[20] YOU C S, PENG X M, ZHANG R. 3D trajectory design for UAV-enabled data harvesting in probabilistic LoS channel[C]//2019 IEEE Global Communications Conference (GLOBECOM). Piscataway:IEEE Press, 2019:1-6.
[21] LIANG Y C, ZENG Y H, PEH E C Y, et al. Sensing-throughput tradeoff for cognitive radio networks[J]. IEEE Transactions on Wireless Communications, 2008, 7(4):1326-1337.
[22] OLABIYI O, ANNAMALAI A. Invertible exponential-type approximations for the Gaussian probability integral Q(x) with applications[J]. IEEE Wireless Communications Letters, 2012, 1(5):544-547.
[23] HU H, ZHANG H, LIANG Y C. On the spectrum- and energy-efficiency tradeoff in cognitive radio networks[J] IEEE Transactions on Communications, 2016, 64(2):490-501.
CJA