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基于机器学习主用户发射模式分类的蜂窝认知无线电网络频谱感知

申滨 王欣 陈思吉 崔太平

申滨, 王欣, 陈思吉, 崔太平. 基于机器学习主用户发射模式分类的蜂窝认知无线电网络频谱感知[J]. 电子与信息学报, 2021, 43(1): 92-100. doi: 10.11999/JEIT191012
引用本文: 申滨, 王欣, 陈思吉, 崔太平. 基于机器学习主用户发射模式分类的蜂窝认知无线电网络频谱感知[J]. 电子与信息学报, 2021, 43(1): 92-100. doi: 10.11999/JEIT191012
Bin SHEN, Xin WANG, Siji CHEN, Taiping CUI. Machine Learning Based Primary User Transmit Mode Classification for Spectrum Sensing in Cellular Cognitive Radio Network[J]. Journal of Electronics & Information Technology, 2021, 43(1): 92-100. doi: 10.11999/JEIT191012
Citation: Bin SHEN, Xin WANG, Siji CHEN, Taiping CUI. Machine Learning Based Primary User Transmit Mode Classification for Spectrum Sensing in Cellular Cognitive Radio Network[J]. Journal of Electronics & Information Technology, 2021, 43(1): 92-100. doi: 10.11999/JEIT191012

基于机器学习主用户发射模式分类的蜂窝认知无线电网络频谱感知

doi: 10.11999/JEIT191012
基金项目: 国家自然科学基金(61571073)
详细信息
    作者简介:

    申滨:男,1978年生,教授,研究方向为大规模MIMO系统、认知无线电等

    王欣:女,1992年生,硕士生,研究方向为认知无线电

    陈思吉:男,1993年生,硕士生,研究方向为认知无线电

    崔太平:男,1981年生,讲师,研究方向为认知无线电、车联网等

    通讯作者:

    申滨 shenbin@cqupt.edu.cn

  • 1) 在CCRN中,由于SUE与其周围的多个蜂窝基站之间的无线链接,假设SUE的位置信息能够通过相应的定位方法较为精确地获得。
  • 中图分类号: TN911

Machine Learning Based Primary User Transmit Mode Classification for Spectrum Sensing in Cellular Cognitive Radio Network

Funds: The National Nature Science Foundation of China (61571073)
  • 摘要:

    近年来,基于机器学习(ML)的频谱感知技术为认知无线电系统提供了新型的频谱状态监测解决方案。利用蜂窝认知无线电网络(CCRN)中的次级用户设备(SUE)所能提供的大量频谱观测数据,该文提出了一种基于主用户(PU)传输模式分类的频谱感知方案。首先,基于多种典型的ML算法,对于网络中的多个主用户发射机(PUT)的传输模式进行分类辨识,在网络整体层面上确定所有PUT的联合工作状态。然后,网络中的SUE根据其所处地理位置或者频谱观测数据,判断其在当前已判定的PUT发射模式下接入授权频谱的可能性。由于PUT在网络中的实际位置可能事先已知或者无法提前确定,该文给出了3种不同的处理方法。理论推导与实验结果表明,所提方案与传统的能量检测方案相比,不仅改善了频谱感知性能,还增加了蜂窝认知网络对于授权频谱的动态访问机会。该方案可以作为蜂窝认知无线电网络中的一种高效实用的频谱感知解决方案。

  • 图  1  仿真场景图

    图  2  PUT数量已知时,其传输模式分类准确率

    图  3  传输功率43 dBm时,8种PUT传输模式下网格标签图

    图  4  PUT传输功率为43 dBm时,网格分类性能

    算法1 基于能量值模板差值的PUT模式分类
     输入:${{{Y}}_m},\widehat {{Y}},G,$阈值$\varphi $
     输出:${\hat {{S}}^{(m)} }$
     初始化
     (1)  ${{{y}}_{m,1} } = {\rm{vec} }({{{Y}}_m})$%矩阵转化为列向量
     (2)  ${{{y}}_{m,2} } = {\rm{sort} }({{{y}}_{m,1} },{\rm{descending} })$%降序排列
     (3)     ${\rm{ = \{ }}{Z_{{x_1},{y_1}}},{Z_{{x_2},{y_2}}}, \cdots ,{Z_{{x_Q},{y_Q}}}\} $
     (4)   获取行位置索引向量 ${{x}}{\rm{ = \{ } }{x_1}{\rm{,} }{x_2}{\rm{,} } ··· {\rm{,} }{x_Q}{\rm{\} } }$及
     (5)     列位置索引向量 ${{y}}{\rm{ = \{ } }{y_1}{\rm{,} }{y_2}{\rm{,} } ··· {\rm{,} }{y_Q}{\rm{\} } }$
     (6)    IF$({Z_{ {x_1},{y_1} } } < \varphi )\& ({Z_{ {x_2},{y_2} } } < \varphi )\& ··· \& ({Z_{ {x_G},{y_G} } } < \varphi )$
     (7)     ${\hat {{S}}^{(m)} } = {\mathbb{S}_0}$
     (8)    Else
     (9)     For $i = 1:1:G$
     (10)      For $j = i + 1:1:G$
     (11)       If $(\left| {{x_i} - {x_j}} \right| < g)\& (\left| {{y_i} - {y_j}} \right| < g)$
     (12)        ${Z_{{x_j},{y_j}}} = 0$
     (13)       EndIF
     (14)      EndFor
     (15)     EndFor
     (16)    EndIF
     (17) For $i = 1:1:G$
     (18)     ${{{h}}_{\rm{1} } }(i) = {\rm{find} }({x_i}\left| { {Z_{ {x_i},{y_i} } } \ne 0} \right.)$
     (19)     ${{{h}}_{\rm{2} } }(i) = {\rm{find} }({y_i}\left| { {Z_{ {x_i},{y_i} } } \ne 0} \right.)$
     (20) EndFor
     (21) ${\varDelta _l} = \displaystyle\sum\limits_{i = 1}^{\left| { {h_1} } \right|} {|{ {{Y} }_m}({ {{h} }_1}(i),{ {{h} }_2}(i)) - { {{Y} }_l}({ {{h} }_1}(i),{ {{h} }_2}(i))|}$
     (22) ${l_{ {\rm{opt} } } } = \mathop {\arg \min }\limits_{l = 1,2, \cdots ,{2^N} - 1} {\varDelta _l}$
     输出:${\hat {{S}}^{(m)} } = {\mathbb{S}_{ {l_{ {\rm{opt} } } } } }$
     注:${\rm{|} }{{{h}}_1}{\rm{|} }$为集合${{{h}}_1}$的势,即其所包含的所有元素的个数。
    下载: 导出CSV

    表  1  CNN分类算法采用的结构参数

    层类型输入尺寸滤波器尺寸激活函数
    卷积层(1)120×1203×3×32ReLu
    卷积层(2)60×603×3×64ReLu
    全连接层14400×11024个神经元Softmax
    下载: 导出CSV

    表  2  PUT传输功率为32 dBm时,PUT传输模式分类准确率

    算法名称数据充分性条件
    11.1%47.4%100%
    能量值模板差值0.120.260.28
    K-means0.420.430.44
    HOG+SVM_8*80.120.160.17
    HOG+SVM_16*160.120.120.18
    CNN0.620.960.99
    下载: 导出CSV
  • ALI A and HAMOUDA W. Advances on spectrum sensing for cognitive radio networks: Theory and applications[J]. IEEE Communications Surveys & Tutorials, 2017, 19(2): 1277–1304. doi: 10.1109/COMST.2016.2631080
    AXELL E, LEUS G, LARSSON E G, et al. Spectrum sensing for cognitive radio: State-of-the-art and recent advances[J]. IEEE Signal Processing Magazine, 2012, 29(3): 101–116. doi: 10.1109/msp.2012.2183771
    黄河, 袁超伟. 基于动态自适应双门限能量检测的序贯协作频谱感知算法[J]. 电子与信息学报, 2018, 40(5): 1037–1043. doi: 10.11999/JEIT170731

    HUANG He and YUAN Chaowei. A sequential cooperative spectrum sensing algorithm based on dynamic adaptive double-threshold energy detection[J]. Journal of Electronics &Information Technology, 2018, 40(5): 1037–1043. doi: 10.11999/JEIT170731
    KIM J and CHOI J P. Sensing coverage-based cooperative spectrum detection in cognitive radio networks[J]. IEEE Sensors Journal, 2019, 19(13): 5325–5332. doi: 10.1109/JSEN.2019.2903408
    申滨, 王志强, 青晗. 基于次用户功率控制辅助的合作频谱感知[J]. 电子与信息学报, 2018, 40(10): 2337–2344. doi: 10.11999/JEIT171232

    SHEN Bin, WANG Zhiqiang, and QING Han. Secondary user power control aided cooperative spectrum sensing[J]. Journal of Electronics &Information Technology, 2018, 40(10): 2337–2344. doi: 10.11999/JEIT171232
    MAUWA H, BAGULA A, ZENNARO M, et al. Systematic analysis of geo-location and spectrum sensing as access methods to TV white space[J]. Journal of ICT Standardization, 2016, 4(2): 147–176. doi: 10.13052/jicts2245-800X.423
    陈思吉, 王欣, 申滨. 一种基于支持向量机的认知无线电频谱感知方案[J]. 重庆邮电大学学报: 自然科学版, 2019, 31(3): 313–322. doi: 10.3979/j.issn.1673-825X.2019.03.005

    CHEN Siji, WANG Xin, and SHEN Bin. A support vector machine based spectrum sensing for cognitive radios[J]. Journal of Chongqing University of Posts and Telecommunications:Natural Science Edition, 2019, 31(3): 313–322. doi: 10.3979/j.issn.1673-825X.2019.03.005
    THILINA K M, CHOI K W, SAQUIB N, et al. Machine learning techniques for cooperative spectrum sensing in cognitive radio networks[J]. IEEE Journal on Selected Areas in Communications, 2013, 31(11): 2209–2221. doi: 10.1109/jsac.2013.131120
    AWE O P and LAMBOTHARAN S. Cooperative spectrum sensing in cognitive radio networks using multi-class support vector machine algorithms[C]. The 9th International Conference on Signal Processing and Communication Systems, Cairns, Australia, 2015: 1–7. doi: 10.1109/ICSPCS.2015.7391780.
    LI Zan, WU Wen, LIU Xiangli, et al. Improved cooperative spectrum sensing model based on machine learning for cognitive radio networks[J]. IET Communications, 2018, 12(19): 2485–2492. doi: 10.1049/iet-com.2018.5245
    LU Yingqi, ZHU Pai, WANG Donglin, et al. Machine learning techniques with probability vector for cooperative spectrum sensing in cognitive radio networks[C]. s2016 IEEE Wireless Communications and Networking Conference, Doha, Qatar, 2016: 1–6. doi: 10.1109/WCNC.2016.7564840.
    CHEN Siji, SHEN Bin, WANG Xin, et al. A strong machine learning classifier and decision stumps based hybrid adaboost classification algorithm for cognitive radios[J]. Sensors, 2019, 19(23): 5077. doi: 10.3390/s19235077
    WEI Zhiqing, FENG Zhiyong, ZHANG Qixun, et al. Three regions for space-time spectrum sensing and access in cognitive radio networks[C]. 2012 IEEE Global Communications Conference, Anaheim, USA, 2012: 1283–1288. doi: 10.1109/GLOCOM.2012.6503290.
    阮丽华, 李勇, 程伟. 一种空时二维联合频谱感知区域划分方案[J]. 系统工程与电子技术, 2016, 38(5): 1146–1152. doi: 10.3969/j.issn.1001-506X.2016.05.27

    RUAN Lihau, LI Yong, and CHENG Wei. Novel region division approach for joint space-time spectrum 2-dimensions sensing in cognitive radio[J]. Systems Engineering and Electronics, 2016, 38(5): 1146–1152. doi: 10.3969/j.issn.1001-506X.2016.05.27
    SINGH G and CHHABRA I. Effective and fast face recognition system using complementary OC-LBP and HOG feature descriptors with SVM classifier[J]. Journal of Information Technology Research, 2018, 11(1): 91–110. doi: 10.4018/JITR.2018010106
    DADI H S, PILLUTLA G K M, and MAKKENA M L. Face recognition and human tracking using GMM, HOG and SVM in surveillance videos[J]. Annals of Data Science, 2018, 5(2): 157–179. doi: 10.1007/s40745-017-0123-2
    MERCHANT K, REVAY S, STANTCHEV G, et al. Deep learning for RF device fingerprinting in cognitive communication networks[J]. IEEE Journal of Selected Topics in Signal Processing, 2018, 12(1): 160–167. doi: 10.1109/JSTSP.2018.2796446
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出版历程
  • 收稿日期:  2019-12-19
  • 修回日期:  2020-03-17
  • 网络出版日期:  2020-09-16
  • 刊出日期:  2021-01-15

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