刘朝阳;王安义;李蓉;

• 1:西安科技大学能源学院
• 2:西安科技大学通信与信息工程学院

摘要(Abstract)：

在非协作通信中,如何在低信噪比的衰落信道下,从截获的信号中提取特征参数,从而准确的判别其调制方式是研究的重点。特征参数的高阶累积量可以去除噪声干扰,针对当前非协作通信领域中低信噪比下调制识别率不高的问题,结合高阶累积量的自身特性,提出新的信号特征提取方法。以Nakagami信道为例,以信号的四阶累积量作为特征参量,并对四阶累积量进行分类门限的划分,主要讨论BPSK,8PSK和16QAM这3种信号在不同符号数下的准确识别率。仿真表明,8PSK在符号数为500时,在信噪比为-5 dB到0 dB的高斯白噪声信道下,识别率在90%以上。

关键词(KeyWords)： 调制识别率;衰落信道;特征提取;高阶累积量

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金青年科学基金项目(61801372)

作者(Author): 刘朝阳;王安义;李蓉;

Email:

DOI: 10.13800/j.cnki.xakjdxxb.2020.0419

参考文献(References)：

• [1] 陈蕙心.通信信号调制识别技术研究[D].西安:西安电子科技大学,2017.CHEN Hui-xin.Research on modulation recognition technology of communication signals[D].Xi'an:Xi'an University of Electronic Science and Technology,2017
• [2] SUN Z,AKYILDIZ I F.Channel modeling and analysis for wireless networks in underground mines and road tunnels[J].IEEE Transactions on Communications,2010,58(6):1758-1768.
• [3] NA D,CHOI K.Intrinsic ICI-free alamouti coded FBMC[J].IEEE Communications Letters,2016,20:1971-1974.
• [4] LI P P,SUN G C,SHEN J Y.Modulation recognition of MQAM signals based on semi supervised clustering theory[J].Applied Mechanics & Materials,2014,519(520):973-976.
• [5] FENG Y X,TENG Z Y,MENG F W,et al.An accurate modulation recognition method of QPSK signal[J].Mathematical Problems in Engineering,2015(1):1-7.
• [6] ANDREJ H,GORAZD K,TOMAZ J.A survey of radio propagation modeling for tunnels[J].IEEE Communications Surveys and Tutorials,2014,16(6):658-667.
• [7] YIN Z D.Research of communication signal modulation scheme recognition based on one-class SVM bayesian algorithm[C]//WiCOM'09 Proceedings of the 5th International Conference on Wireless communications,networking and mobile.NJ,USA:IEEE Press Piscataway,2009:739-742.
• [8] 郗茜.基于LS-SVM的半圆拱形巷道无线信道建模与预测[D].西安:西安科技大学,2017.YUN Xi.Modeling and prediction of wireless channel in semi-circular arch roadway based on LS-SVM[D].Xi'an:Xi'an University of Science and Technology,2017.
• [9] 王安义.最小二乘支持向量机在信道均衡中的应用[J].西安科技大学学报,2014,34(5):591-596.WANG An-yi.Application of LSSVM in channel equalization[J].Journal of Xi'an University of Science and Technology,2014,34(5):591-596.
• [10] 陈柳,吴冬梅.小波分析及支持向量机应用于大气污染预测[J].西安科技大学学报,2010,30(6),726-729.CHEN Liu,WU Dong-mei.Application of wavelet analysis and support vector machine to air pollution prediction[J].Journal of Xi'an University of Science and Technology,2010,30(6),726-729.
• [11] 李朋林,赵晶,马琰玲.基于SVM的人才资源对陕西经济增长影响的预测模型研究[J].西安科技大学学报,2012,32(5):637-642.LI peng-lin,ZHAO Jing,MA Yan-ling.Study on the prediction model of the impact of human resources on Shaanxi economic growth based on SVM[J].Journal of Xi'an University of Science and Technology,2012,32(5):637-642.
• [12] EBRAHIMZADEH A,HOSSIENZADEH M.A novel method using GA-based Clustering and spectral features for modulation classification[C]//2011 International Conference on Electrical and Control Engineering,Yichang,2011:4705-4708.
• [13] HO K M,VAZ C,DAUT D G.Automatic classification of amplitude,frequency,and phase shift keyed signals in the Wavelet Domain[C]//2010 IEEE Sarnoff Symposium,Princeton N J,2010:1-6.
• [14] 周龙梅.基于深度学习的通信信号识别技术研究[D].北京:北京邮电大学,2018.ZHOU Long-mei.Research on communication signal recognition technology based on deep learning[D].Beijing:Beijing University of Posts and Telecommunications,2018.
• [15] GHASEMI S,GANGAL A.An effective algorithm for automatic modulation recognition[C]//2014 22nd Signal Processing and Communications Applications Conference,Trabzon,2014:903-906.
• [16] BOUTTE D,SANTHANAM B.A hybrid ICA-SVM approach to continous phase modulation recognition[J].Signal Processing Letters,2009,16(5):402-405.
• [17] TENG X Y,TIAN P W,YU H Y.Modulation classification based on spectral correlation and SVM[C]//WiCOM,2008:1-4.
• [18] WANG L X,REN Y J,ZHANG R H.Algorithm of digital modulation recognition based on support vector machine[C]//ICMLC,2009:980-983.
• [19] ZHAO F C,HU Y H.Classification using wavelet packet decomposition and SVM fuzzy network for digital modulations in satellite communication[C]//SIPS,2007:562-566.
• [20] LI P H,ZHANU H X,WAND X Y.Modulation recognition of communication signals based on high order cumulants and support vector machine[J].The Journal of China Universities of Posts and Telecommunications,2012,19(11):61-65.
• [21] CHEOL-SUN PARK,WON JANG,SUN-PHIL NAH,et al.Automatic modulation recognition of digital signals using wavelet features and SVM[C]//ICACT,2008:387-390.
• [22] 褚国星.非协作通信信号调制识别研究与实现[D].重庆:重庆大学,2017.CHU Guo-xing.Research and implementation of modulation recognition for non-cooperative communication signals[D].Chongqing:Chongqing University,2017.
• [23] WANG T,WEN C K,WANG H,et al.Deep learning for wireless physical layer:opportunities and challenges[J].China Communications,2017,14(11):92-111.
• [24] D?RNER S,CAMMERER S,HOYDIS J,et al.Deep learning based communication over the air[J].IEEE Journal of Selected Topics in Signal Processing,2018,12(1):132-143.
• [25] OSAMA M,ABO S.Propagation of electromagnetic waves in a rectangular tunnel[J].IEEE Applied Mathematics and Computation,2003,136:405-413.
• [26] DERYCK L.Natural propagation of electromagnetic waves in tunnels[J].IEEE Transactions on Vehicular Technology,1978,27(3):145-150.
• [27] 李艳玲.非合作通信中单载波调制信号识别算法研究[D].西安:西安电子科技大学,2013.LI Yan-ling.Research on single carrier modulation signal recognition algorithm in non-cooperative communication[D].Xi'an:Xi'an University of Electricity and Technology,2013.