留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

一种无人机数据链信道选择和功率控制方法

张文秋 丁文锐 刘春辉

张文秋, 丁文锐, 刘春辉等 . 一种无人机数据链信道选择和功率控制方法[J]. 北京航空航天大学学报, 2017, 43(3): 583-591. doi: 10.13700/j.bh.1001-5965.2016.0166
引用本文: 张文秋, 丁文锐, 刘春辉等 . 一种无人机数据链信道选择和功率控制方法[J]. 北京航空航天大学学报, 2017, 43(3): 583-591. doi: 10.13700/j.bh.1001-5965.2016.0166
ZHANG Wenqiu, DING Wenrui, LIU Chunhuiet al. A channel selection and power control method of UAV data link[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(3): 583-591. doi: 10.13700/j.bh.1001-5965.2016.0166(in Chinese)
Citation: ZHANG Wenqiu, DING Wenrui, LIU Chunhuiet al. A channel selection and power control method of UAV data link[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(3): 583-591. doi: 10.13700/j.bh.1001-5965.2016.0166(in Chinese)

一种无人机数据链信道选择和功率控制方法

doi: 10.13700/j.bh.1001-5965.2016.0166
基金项目: 

国家“863”计划 2013AA122101

详细信息
    作者简介:

    张文秋, 女, 硕士研究生。主要研究方向:信号与信息处理

    丁文锐, 女, 博士, 研究员, 博士生导师。主要研究方向:图像处理和自适应信号处理

    刘春辉, 男, 博士, 工程师。主要研究方向:自适应信号处理

    通讯作者:

    丁文锐, E-mail:ding@buaa.edu.cn

  • 中图分类号: TN973

A channel selection and power control method of UAV data link

Funds: 

National High-tech Research and Development Program of China 2013AA122101

More Information
  • 摘要:

    无人机(UAV)数据链在复杂电磁和地理自然环境中可靠性受到严重威胁,针对如何通过选择信道和调整信号发送功率保证UAV通信质量的问题,提出了一种结合相关向量回归(RVR)的信道选择和功率控制方法。方法采用RVR建立干扰信息、误码率(BER)与信噪比(SNR)的映射模型,通过该模型可根据实时干扰参数,预测信道满足UAV数据链BER要求的最小化SNR,进而可计算最小化的发送功率,把最小化功率作为标准判断信道质量好坏,选择信道的同时确定发送功率,简化过程,以最小化信道发送功率达到抗干扰的目的。仿真实验证明,该方法能够有效选择可用信道并调整发送功率,抑制干扰,时间和能量开销低,具有较强实用性。

     

  • 图 1  随机二元码干扰BER仿真

    Figure 1.  BER simulation of random binary code interference

    图 2  无人机数据链信道选择与功率控制方法示意图

    Figure 2.  Schematic of a channel selection and power control method for UAV data link

    图 3  SNR预测模型建立流程图

    Figure 3.  Flowchart of SNR prediction model establishment

    图 4  各信道预测发送功率最小值pmin

    Figure 4.  Minimum predicted transmit power pmin for every channel

    图 5  各信道误码率的RPE值

    Figure 5.  RPE value of BER for every channel

    图 6  仿真实验地理环境

    Figure 6.  Geographical environment of simulation experiment

    图 7  干扰功率分布

    Figure 7.  Distribution of interference power

    图 8  各信道预测SNR与飞行距离的关系

    Figure 8.  Relationship between predicted SNR and flight distance for every channel

    图 9  各信道pmin与飞行距离的关系

    Figure 9.  Relationship between pmin and flight distance for every channel

    表  1  RVR和SVR训练结果

    Table  1.   Training results of RVR and SVR

    训练结果 RVR SVR
    高斯 5阶多项式 Laplace RBF 多项式
    RVs,SVs 71 73 3 564 604 1 404
    RMSE 0.023 1 0.020 3 0.062 6 0.074 0.58
    RPE/% 0.198 0.180 0.325 0.887 5.17
    下载: 导出CSV

    表  2  采用PCA降维后RVR训练结果

    Table  2.   Training results of RVR after dimensionality reduction using PCA

    训练结果 RVR (高斯核函数)
    特征向量为6维 特征向量为5维 特征向量为4维
    RMSE 0.301 0.302 6.479
    RPE/% 4.30 4.56 24.65
    下载: 导出CSV

    表  3  不同标准下的信道质量排序

    Table  3.   Channel quality ranking based on different criteria

    INR1/dB pmin标准 SINR标准 BER标准
    -6~-2.5 1-4-3-5-2 1-4-3-5-2 1-4-3-5-2
    -2.5~-1.5 1-4-3-5-2 1-4-3-2-5 1-4-3-5-2
    -1.5~0 1-4-5-3-2 1-4-3-2-5 1-4-5-3-2
    0~0.5 1-4-5-2-3 1-4-3-2-5 1-4-5-2-3
    0.5~2 1-4-5-2-3 1-4-2-3-5 1-4-5-2-3
    2~3.5 1-4-2-5-3 1-4-2-3-5 1-4-2-5-3
    3.5~6 1-4-2-5-3 1-4-2-5-3 1-4-2-5-3
    下载: 导出CSV

    表  4  信道检测次数对比

    Table  4.   Comparison of channel detected iterations

    检测方法 不考虑信道5 考虑信道5
    随机检测 相关检测 随机检测 相关检测
    检测次数 1.67 1.5 2 1.6
    下载: 导出CSV

    表  5  信道选择和功率控制方法仿真结果

    Table  5.   Simulation results of channel selection and power control method

    信道 位置1 位置2 位置3 位置4 位置5 位置6
    pmin/dBm BER/dB pmin/dBm BER/dB pmin/dBm BER/dB pmin/dBm BER/dB pmin/dBm BER/dB pmin/dBm BER/dB
    1 18.72 -1.98 15.12 -1.93 11.80 -1.97 13.55 -1.96 17.39 -1.99 20.40 -2.00
    2 27.49 -1.94 25.56 -1.99 24.27 -1.98 25.69 -2.01 27.74 -2.00 29.91 -2.00
    3 25.77 -2.02 22.74 -1.98 20.23 -2.01 23.09 -2.01 28.74 -2.02 34.08 -1.98
    4 23.60 -1.99 20.07 -2.02 16.84 -2.01 18.69 -2.01 23.00 -2.02 26.98 -2.00
    5 30.22 -1.99 26.62 -2.00 23.28 -1.99 24.94 -2.00 28.77 -1.98 31.78 -1.98
    注:BER取对数,故单位为dB。
    下载: 导出CSV
  • [1] VACHTSEVANOS G J, VALAVANIS K P.Military and civilian unmanned aircraft[M]//Handbook of unmanned aerial vehicles.Berlin:Springer Netherlands, 2015:93-103.
    [2] ⅡDUKA H.Fixed point optimization algorithm and its application to power control in CDMA data networks[J].Mathematical Programming, 2012, 133(1):227-242.
    [3] ZENGEN G, BUESCHING F, POETTNER W B, et al.Adaptive channel selection for interference reduction in wireless sensor networks[C]//Proceedings, ARCS 2015-The 28th International Conference on Architecture of Computing Systems.Nuremberg:VDE, 2015:1-7.
    [4] SKOKOWSKI P, MALON K, KELNER J M, et al.Adaptive channels' selection for hierarchical cluster based cognitive radio networks[C]//2014 8th International Conference on Signal Processing and Communication Systems (ICSPCS).Piscataway, NJ:IEEE Press, 2014:1-6.
    [5] PAL A, NASIPURI A.A distributed channel selection scheme for multi-channel wireless sensor networks[C]//Proceedings of the thirteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing.New York:ACM, 2012:263-264.
    [6] XIAO L, DAI H, NING P. Jamming-resistant collaborative broadcast using uncoordinated frequency hopping[J].IEEE Transactions on Information Forensics and Security, 2012, 7(1):297-309. doi: 10.1109/TIFS.2011.2165948
    [7] MORIMOTO A, MIKI N, ISHⅡ H, et al.Investigation on transmission power control in heterogeneous network employing cell range expansion for LTE-Advanced uplink[C]//2012 18th European Wireless Conference European Wireless, EW.Nuremberg:VDE, 2012:1-6.
    [8] 李思佳, 毛玉泉, 郑秋容, 等.UAV数据链抗干扰的关键技术研究综述[J].计算机应用研究, 2011, 28(6):2020-2024. http://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ201106007.htm

    LI S J, MAO Y Q, ZHENG Q R, et al.Overview of research on key techniques for anti-jamming of UAV data link[J].Application Research of Computers, 2011, 28(6):2020-2024(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ201106007.htm
    [9] BACCOUR N, KOUBAA A, MOTTOLA L, et al.Radio link quality estimation in wireless sensor networks:A survey[J].ACM Transactions on Sensor Networks (TOSN), 2012, 8(4):34.
    [10] WANG Y, MARTONOSI M, PEH L S.Predicting link quality using supervised learning in wireless sensor networks[J].ACM SIGMOBILE Mobile Computing and Communications Review, 2007, 11(3):71-83. doi: 10.1145/1317425
    [11] 李建东, 郭梯云, 邬国扬.移动通信[M].4版.西安:西安电子科技大学出版社, 2009:94-132.

    LI J D, GUO T Y, WU G Y.Mobile communication[M].4th ed.Xi'an:Xidian University Press, 2009:94-132(in Chinese).
    [12] PHILLIPS C, SICKER D, GRUNWALD D.The stability of the Longley-Rice irregular terrain model for typical problems:CU-CS-1086-11[R].Boulder:University of Colorado at Boulder, 2011.
    [13] VALAVANIS K P, VACHTSEVANOS G J.Handbook of unmanned aerial vehicles[M].[S.l.]:Springer Publishing Company, Incorporated, 2014:749-844.
    [14] 崔蓉. 基于序贯决策的无线传感网络频谱感知策略与分配方法[D]. 北京: 北京邮电大学, 2015: 18-29.

    CUI R.Wireless sensor network spectrum sensing and allocation strategy based on sequential decision[D].Beijing:Beijing University of Posts and Communications, 2015:18-29(in Chinese).
    [15] BASAK D, PAL S, PATRANABIS D C.Support vector regression[J].Neural Information Processing-Letters and Reviews, 2007, 11(10):203-224.
    [16] TIPPING M E.Sparse Bayesian learning and the relevance vector machine[J].The Journal of Machine Learning Research, 2001, 1:211-244.
    [17] CAMPS-VALLS G, MARTíNEZ-RAMÓN M, ROJO-ÁLVAREZ J L, et al.Nonlinear system identification with composite relevance vector machines[J].IEEE Signal Processing Letters, 2007, 14(4):279-282. doi: 10.1109/LSP.2006.885290
    [18] NICOLAOU M A, GUNMES H, PANTIC M.Output-associative RVM regression for dimensional and continuous emotion prediction[J].Image and Vision Computing, 2012, 30(3):186-196. doi: 10.1016/j.imavis.2011.12.005
    [19] TANTUM S L, SCOTT W R, MORTON K D, et al.Target classification and identification using sparse model representations of frequency-domain electromagnetic induction sensor data[J].IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(5):2689-2706. doi: 10.1109/TGRS.2012.2215876
    [20] JOLLIFFE I T.Principal component analysis[M].Berlin:Springer, 2002:41-64.
  • 加载中
图(9) / 表(5)
计量
  • 文章访问数:  1287
  • HTML全文浏览量:  90
  • PDF下载量:  555
  • 被引次数: 0
出版历程
  • 收稿日期:  2016-03-07
  • 录用日期:  2016-06-03
  • 网络出版日期:  2017-03-20

目录

    /

    返回文章
    返回
    常见问答