Advanced Search
Volume 42 Issue 8
Aug.  2020
Turn off MathJax
Article Contents
Xinyu DA, Hongwei ZHANG, Hang HU, Yu PAN, Jinling JING. Throughput Optimization of Secondary Link in Cognitive UAV Network[J]. Journal of Electronics & Information Technology, 2020, 42(8): 1934-1941. doi: 10.11999/JEIT200056
Citation: Xinyu DA, Hongwei ZHANG, Hang HU, Yu PAN, Jinling JING. Throughput Optimization of Secondary Link in Cognitive UAV Network[J]. Journal of Electronics & Information Technology, 2020, 42(8): 1934-1941. doi: 10.11999/JEIT200056

Throughput Optimization of Secondary Link in Cognitive UAV Network

doi: 10.11999/JEIT200056
Funds:  The National Natural Science Foundation of China (61571460, 61901509, 61671475), The 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)
  • Received Date: 2020-01-14
  • Rev Recd Date: 2020-04-30
  • Available Online: 2020-07-08
  • Publish Date: 2020-08-18
  • The application of Unmanned Air Vehicles (UAV)-enabled Cognitive Radio (CR) is widely used due to the convenience and high mobility of the UAV. In the UAV-based Cognitive Radio Network (CRN), the throughput optimization scheme in single radian is firstly investigated, in which the sensing radian is optimized to maximize the average throughput of UAV. Then, a multi-radian throughput optimization scheme based on Cooperative Spectrum Sensing (CSS) is studied to improve the sensing performance under the non-ideal channel, and the throughput of the UAV is maximized by utilizing an Alternative Iterative Optimization (AIO) algorithm. The simulation results show that the proposed scheme has better performance on improving the throughput of the UAV and ensuring the Quality-of-Service (QoS) of the Primary User (PU) when the channel fading is serious.

  • loading
  • NIU Haoran, GONZALEZ-PRELCIC N, and HEATH R W. A UAV–based traffic monitoring system–invited paper[C]. The 87th IEEE Vehicular Technology Conference (VTC Spring). Porto, Portugal, 2018: 1–5. doi: 10.1109/vtcspring.2018.8417546.
    高杨, 李东生, 程泽新. 无人机分布式集群态势感知模型研究[J]. 电子与信息学报, 2018, 40(6): 1271–1278. doi: 10.11999/JEIT170877

    GAO Yang, LI Dongsheng, and CHENG Zexin. UAV distributed swarm situation awareness model[J]. Journal of Electronics &Information Technology, 2018, 40(6): 1271–1278. doi: 10.11999/JEIT170877
    倪磊, 达新宇, 王舒, 等. 基于物理层信息加密的卫星隐蔽通信研究[J]. 工程科学与技术, 2018, 50(1): 133–139. doi: 10.15961/j.jsuese.201700160

    NI Lei, DA Xinyu, WANG Shu, et al. Research on satellite covert communication based on the information encryption of physical layer[J]. Advanced Engineering Sciences, 2018, 50(1): 133–139. doi: 10.15961/j.jsuese.201700160
    赵太飞, 许杉, 屈瑶, 等. 基于无线紫外光隐秘通信的侦察无人机蜂群分簇算法[J]. 电子与信息学报, 2019, 41(4): 967–972. doi: 10.11999/JEIT180491

    ZHAO Taifei, XU Shan, QU Yao, et al. Cluster–based algorithm of reconnaissance UAV swarm based on wireless ultraviolet secret communication[J]. Journal of Electronics &Information Technology, 2019, 41(4): 967–972. doi: 10.11999/JEIT180491
    GUPTA A and JHA R K. A survey of 5G network: Architecture and emerging technologies[J]. IEEE Access, 2015, 3: 1206–1232. doi: 10.1109/ACCESS.2015.2461602
    SULTANA A, ZHAO Lian, and FERNANDO X. Energy–efficient power allocation in underlay and overlay cognitive device–to–device communications[J]. IET Communications, 2019, 13(2): 162–170. doi: 10.1049/iet-com.2018.5464
    LI He, OTA K, and DONG Mianxiong. Learning IoT in Edge: Deep learning for the internet of things with edge computing[J]. IEEE Network, 2018, 32(1): 96–101. doi: 10.1109/MNET.2018.1700202
    SALEEM Y, REHMANI M H, and ZEADALLY S. Integration of cognitive radio technology with unmanned aerial vehicles: Issues, opportunities, and future research challenges[J]. Journal of Network and Computer Applications, 2015, 50: 15–31. doi: 10.1016/j.jnca.2014.12.002
    NI Lei, Da Xinyu, HU Hang, et al. Outage constrained robust transmit design for secure cognitive radio with practical energy harvesting[J]. IEEE Access, 2018, 6: 71444–71454. doi: 10.1109/ACCESS.2018.2881477
    XU Wenbo, WANG Shu, YAN Shu, 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. doi: 10.1109/JIOT.2018.2882532
    AQUINO G P, GUIMARÃES D A, MENDES L L, et al. Combined pre–distortion and censoring for bandwidth–efficient and energy–efficient fusion of spectrum sensing information[J]. Sensors, 2017, 17(3): 654. doi: 10.3390/s17030654
    FAN Lisheng, LEI Xianfu, YANG Nan, et al. Secrecy cooperative networks with outdated relay selection over correlated fading channels[J]. IEEE Transactions on Vehicular Technology, 2017, 66(8): 7599–7603. doi: 10.1109/TVT.2017.2669240
    KISHORE R, GURUGOPINATH S, MUHAIDAT S, et al. Sensing–throughput tradeoff for superior selective reporting–based spectrum sensing in energy harvesting HCRNs[J]. IEEE Transactions on Cognitive Communications and Networking, 2019, 5(2): 330–341. doi: 10.1109/TCCN.2019.2906915
    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). Dallas, USA, 2018: 760–768. doi: 10.1109/ICUAS.2018.8453329.
    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. doi: 10.1109/ACCESS.2017.2695586
    ZHENG Yi, WANG Yuwen, and MENG Fanji. Modeling and simulation of pathloss and fading for air–ground link of HAPs within a network simulator[C]. 2013 International Conference on Cyber–Enabled Distributed Computing and Knowledge Discovery. Beijing, China, 2013: 421–426. doi: 10.1109/CyberC.2013.78.
    AL–HOURANI A, KANDEEPAN S, and LARDNER S. Optimal LAP altitude for maximum coverage[J]. IEEE Wireless Communications Letters, 2014, 3(6): 569–572. doi: 10.1109/lwc.2014.2342736
    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). San Diego, USA, 2015: 1–6. doi: 10.1109/GLOCOM.2015.7417609.
    GHAZZAI H, GHORBEL M B, 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. doi: 10.1109/TGCN.2017.2750721
    LIANG Yingchang, ZENG Yonghong, PEH E C Y, et al. Sensing–throughput tradeoff for cognitive radio networks[J]. IEEE Transactions on Wireless Communications, 2008, 7(4): 1326–1337. doi: 10.1109/twc.2008.060869
    LIU Liang, ZHANG Shuowen, and ZHANG Rui. CoMP in the Sky: UAV placement and movement optimization for multi–user communications[J]. IEEE Transactions on Communications, 2019, 67(8): 5645–5658. doi: 10.1109/TCOMM.2019.2907944
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(8)  / Tables(2)

    Article Metrics

    Article views (2167) PDF downloads(87) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return