基于最小均方差拟合的QAM调制识别器
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摘要
针对QAM(Quadrature Amplitude Modulation)识别中存在着识别率受信道参数和噪声影响大、低信噪比条件下识别率不高等问题,提出一种基于最小均方差拟合的识别方法。根据不同QAM调制的信号特征,找到了其对应的特征序列,推导了该特征序列的概率密度函数(pdf)和经验累积分布函数(cdf),并对其进行折叠处理,构造了基于最小均方差拟合的折叠cdf识别方法。仿真结果表明,该方法能够在瑞利信道和高斯白噪声下应用,且该方法在低信噪比下识别概率较高,能够应用于电子战,电子干扰和频谱检测等工程领域中。
For QAM( Quadrature Amplitude Modulation) recognition,there is a problem that the recognition rate is affected by the channel parameters and noise,the recognition rate is low under low SNR conditions,and a recognition method based on the minimum mean square error fitting is proposed. According to different QAM modulations,the signal features were found and their corresponding feature sequences were found. The probability density function( pdf) and empirical cumulative distribution function( cdf) of the feature sequence were deduced and folded,and the folding based on the minimum mean square error fitting was constructed. The cdf identification method. The simulation results show that this method can be applied in a Rayleigh channel and Gaussian white noise,and the method has high recognition probability under low signal to SNR. It can be applicable to electronic warfare,electronic jamming and spectrum detection and other engineering fields.
For QAM( Quadrature Amplitude Modulation) recognition,there is a problem that the recognition rate is affected by the channel parameters and noise,the recognition rate is low under low SNR conditions,and a recognition method based on the minimum mean square error fitting is proposed. According to different QAM modulations,the signal features were found and their corresponding feature sequences were found. The probability density function( pdf) and empirical cumulative distribution function( cdf) of the feature sequence were deduced and folded,and the folding based on the minimum mean square error fitting was constructed. The cdf identification method. The simulation results show that this method can be applied in a Rayleigh channel and Gaussian white noise,and the method has high recognition probability under low signal to SNR. It can be applicable to electronic warfare,electronic jamming and spectrum detection and other engineering fields.
引文
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[5] Hameed F,Dobre O A,Popescu D C. On the likelihoodbased approach to modulation classification[J]. IEEE Transactions on Wireless Communications, 2009, 8(12):5884-5892.
[6] Abu-Romoh M,Aboutaleb A,Rezki Z. Automatic Modulation Classifcation Using Moments And Likelihood Maximization[J]. IEEE Communications Letters,2018:1-1.
[7] Abdelbar M,Tranter W H,Bose T. Cooperative Cumulants-Based Modulation Classification in Distributed Networks[J]. 2018,PP(99):1-1.
[8] Zhang J,Wang F,Zhong Z,et al. Continuous Phase Modulation Classification via Baum-Welch Algorithm[J].IEEE Communications Letters,2018,PP(99):1-1.
[9] Swami A,Sadler B M. Hierarchical Digital Modulation Classification Using Cumulants[J]. IEEE Trans Commun,2000,48(3):416-429.Wu H C,Saquib M,Yun Z. Novel Automatic Modulation Classification Using Cumulant Features for Communications via Multipath
[10] Channels[J]. IEEE Transactions on Wireless Communications,2008,7(8):3098-3105.
[11] Dobre O A,Oner M,Rajan S,et al. CyclostationarityBased Robust Algorithms for QAM Signal Identification[J]. IEEE Communicati-ons Letters,2012,16(1):12-15.
[12] Chavali V G,Silva C R C M D. Classification of Digital Amplitude-Phase Modulated Signals in Time-Correlated Non-Gaussian Cha-nnels[J]. IEEE Transactions on Communications,2013,61(6):2408-2419.
[13] Wang F,Wang X. Fast and Robust Modulation Classification via Kolmogorov-Smirnov Test[J]. IEEE Transactions on Communicatio-ns,2010,58(8):2324-2332.
[14] Urriza P,Rebeiz E,Pawelczak P,et al. Computationally Efficient Modulation Level Classification Based on Probability Distribution Distance Functions[J]. IEEE Communications Letters,2011,15(5):476-478.
[15] Wang S,Sun Z,Liu S,et al. Modulation Classification of Linear Digital Signals Based on Compressive Sensing Using High-Order M-oments[C]//Modelling Symposium. IEEE,2014:145-150.
[16] Aslam M W,Zhu Z,Nandi A K. Automatic Modulation Classification Using Combination of Genetic Programming and KNN[J]. I-EEE Transactions on Wireless Communications,2012,11(8):2742-2750.
[17] Han L,Gao F,Li Z,et al. Low Complexity Automatic Modulation Classification Based on Order-Statistics[J].IEEE Transactions on Wireless Communications,2017,16(1):400-411.
[18] O’Shea T J,Corgan J,Clancy T C. Convolutional Radio Modulation Recognition Networks[J]. Communications in Computer and Information Science,2016:213-226.
[19] Liu X,Yang D,Gamal A E. Deep Neural Network Architectures for Modulation Classification[J]. 2017.
[20] A. Swami and B. M. Sadler,“Hierarchical digital modulation clas-sification using cumulants,” IEEE Trans.Commun.,vol. 48,no. 3,pp. 416-429,Mar. 2000.
[2] Marey M,Dobre O A. Blind Modulation Classification Algorithm for Single and Multiple-Antenna Systems Over Frequency-Selective Channels[J]. IEEE Signal Processing Letters,2014,21(9):1098-1102.
[3] Dobre O. Signal identification for emerging intelligent radios:classical problems and new challenges[J]. Instrumentation&Measureme-nt Magazine IEEE,2015,18(2):11-18.
[4] Wei,Wen,and J. M. Mendel."Maximum-likelihood classification for digital amplitude-phase modulations."IEEE Transactions 48. 2(2000):189-193.
[5] Hameed F,Dobre O A,Popescu D C. On the likelihoodbased approach to modulation classification[J]. IEEE Transactions on Wireless Communications, 2009, 8(12):5884-5892.
[6] Abu-Romoh M,Aboutaleb A,Rezki Z. Automatic Modulation Classifcation Using Moments And Likelihood Maximization[J]. IEEE Communications Letters,2018:1-1.
[7] Abdelbar M,Tranter W H,Bose T. Cooperative Cumulants-Based Modulation Classification in Distributed Networks[J]. 2018,PP(99):1-1.
[8] Zhang J,Wang F,Zhong Z,et al. Continuous Phase Modulation Classification via Baum-Welch Algorithm[J].IEEE Communications Letters,2018,PP(99):1-1.
[9] Swami A,Sadler B M. Hierarchical Digital Modulation Classification Using Cumulants[J]. IEEE Trans Commun,2000,48(3):416-429.Wu H C,Saquib M,Yun Z. Novel Automatic Modulation Classification Using Cumulant Features for Communications via Multipath
[10] Channels[J]. IEEE Transactions on Wireless Communications,2008,7(8):3098-3105.
[11] Dobre O A,Oner M,Rajan S,et al. CyclostationarityBased Robust Algorithms for QAM Signal Identification[J]. IEEE Communicati-ons Letters,2012,16(1):12-15.
[12] Chavali V G,Silva C R C M D. Classification of Digital Amplitude-Phase Modulated Signals in Time-Correlated Non-Gaussian Cha-nnels[J]. IEEE Transactions on Communications,2013,61(6):2408-2419.
[13] Wang F,Wang X. Fast and Robust Modulation Classification via Kolmogorov-Smirnov Test[J]. IEEE Transactions on Communicatio-ns,2010,58(8):2324-2332.
[14] Urriza P,Rebeiz E,Pawelczak P,et al. Computationally Efficient Modulation Level Classification Based on Probability Distribution Distance Functions[J]. IEEE Communications Letters,2011,15(5):476-478.
[15] Wang S,Sun Z,Liu S,et al. Modulation Classification of Linear Digital Signals Based on Compressive Sensing Using High-Order M-oments[C]//Modelling Symposium. IEEE,2014:145-150.
[16] Aslam M W,Zhu Z,Nandi A K. Automatic Modulation Classification Using Combination of Genetic Programming and KNN[J]. I-EEE Transactions on Wireless Communications,2012,11(8):2742-2750.
[17] Han L,Gao F,Li Z,et al. Low Complexity Automatic Modulation Classification Based on Order-Statistics[J].IEEE Transactions on Wireless Communications,2017,16(1):400-411.
[18] O’Shea T J,Corgan J,Clancy T C. Convolutional Radio Modulation Recognition Networks[J]. Communications in Computer and Information Science,2016:213-226.
[19] Liu X,Yang D,Gamal A E. Deep Neural Network Architectures for Modulation Classification[J]. 2017.
[20] A. Swami and B. M. Sadler,“Hierarchical digital modulation clas-sification using cumulants,” IEEE Trans.Commun.,vol. 48,no. 3,pp. 416-429,Mar. 2000.