OFDM信道系统下数字信号调制识别研究
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摘要
通信信号传输环境日益密集,通信领域中各种技术的更新需求不断增加,促使信号处理中调制、发射、接收、识别等技术朝着性能更优、系统复杂度渐简的方向发展。信号的调制识别技术是通信系统中关键技术之一,是国内外学者几十年来致力研究的重要课题,也提出了很多不同的调制识别算法,总结起来可以分为两大类：建立在概率论、随机过程和数理统计等数学理论基础之上的判决论识别法和基于特征值的统计模式识别法。由于判决论识别法需要大量繁琐的先验估计知识,因此统计模式识别法成为近几年研究的主流。
本论文以降低系统复杂度和提高系统抗噪性能为主导思想,基于统计模式识别法的分析思路,针对性的研究OFDM信道传输系统下的数字信号调制识别问题,重点对ASK、FSK、PSK三种信号的类间识别特征值提取和识别性能和MPSK信号调制阶数识别的特征值提取和识别性能问题进行分析和仿真。论文取得的主要研究成果如下：
1.对OFDM系统做了详细分析,以QPSK调制信号为例,设置该系统传输子载波数为128,对其传输模型和传输性能进行仿真,验证该系统的高频谱利用率和良好的抗噪性能。
2.基于谱相关分析的基础上,提出了相关谱谱峰个数m和相关谱归一化平均能量H相结合的特征值,对AS、FSK、PSK三种信号进行类间识别。论文根据所提取的特征值构建调制识别流程,并对识别性能进行计算机仿真,验证所提取特征值的有效性和识别算法的性能。
3.基于高阶累量分析的基础上,对MPSK信号的二阶累量和四阶累量进行计算,提出新的调制识别特征值向量,针对性地对MPSK信号的调制阶数进行分类。论文根据MPSK信号的特征向量,对其算法流程进行描述,并使用MATLAB软件对所提取特征值的识别性能进行仿真,验证其识别性能。
With the transmission environment of communication signal become more and more intensive, the demand of updating technologies in the communication field is increasing. So the technologies such as modulation, transmission, classification are developing toward the orientation of better performance and more simple system. Modulation classification is one of the key technologies in communication system. It's an important issue, which have many scholars have been studying for decades. There have already been lots of different modulation classification algorithms, which fall into two categories:decision theoretic and statistical pattern recognition. Decision theoretic is based on the theory of probability judgment, stochastic processes and so on. This algorithm requires a lot of tedious knowledge which is hard to get access to. Consequently, the statistical pattern recognition has become a mainstream in recent years.
In order to reduce the complexity of the system and improve the performance, this dissertation bases on the analysis of previous works in the theory of statistical pattern recognition, researches the modulation classification of digital signal on the basis of OFDM system, and focuses on the modulated signal of ASK, FSK, PSK, and specially focus on MPSK. In this dissertation, we abstract some features to classify these signals and prove the performance of OFDM system. And the simulation results are also given in some chapters and sections.
Our main research results can be summarized mainly as follows:
1. The dissertation gives a detailed analysis of OFDM system and takes QPSK as an example, sets the transmit number of sub-carriers as128, and simulate the performance of transmission system to verify the system's high spectrum efficiency and good anti-noise performance.
2. Based on spectral analysis, this dissertation extracts two features as the parameter to identify ASK, FSK, PSK from each other, the two features are the number of spectral peaks as m, and the normalized spectrum average energy as H. We draw a flowcharts of modulation classification process and simulate the performance of two features and also the performance of the recognition algorithm as well.
3. With the modulation of MPSK based on OFDM channel system, the deduction and analysis of the second-order cumulants and the fourth-order cumulants of MPSK signal, An innovative feature to distinguish the modulation order of the MPSK is proposed. And this feature, due to its simple extracted approach and easy realization, can efficiently achieve the goal of modulation classification. This dissertation makes a comparison of the simulation of the MPSK recognition rate. The modulation algorithm is based on high-order cumulants. And the result shows that the advantage of high-order cumulants analysis is verified.
本论文以降低系统复杂度和提高系统抗噪性能为主导思想,基于统计模式识别法的分析思路,针对性的研究OFDM信道传输系统下的数字信号调制识别问题,重点对ASK、FSK、PSK三种信号的类间识别特征值提取和识别性能和MPSK信号调制阶数识别的特征值提取和识别性能问题进行分析和仿真。论文取得的主要研究成果如下：
1.对OFDM系统做了详细分析,以QPSK调制信号为例,设置该系统传输子载波数为128,对其传输模型和传输性能进行仿真,验证该系统的高频谱利用率和良好的抗噪性能。
2.基于谱相关分析的基础上,提出了相关谱谱峰个数m和相关谱归一化平均能量H相结合的特征值,对AS、FSK、PSK三种信号进行类间识别。论文根据所提取的特征值构建调制识别流程,并对识别性能进行计算机仿真,验证所提取特征值的有效性和识别算法的性能。
3.基于高阶累量分析的基础上,对MPSK信号的二阶累量和四阶累量进行计算,提出新的调制识别特征值向量,针对性地对MPSK信号的调制阶数进行分类。论文根据MPSK信号的特征向量,对其算法流程进行描述,并使用MATLAB软件对所提取特征值的识别性能进行仿真,验证其识别性能。
With the transmission environment of communication signal become more and more intensive, the demand of updating technologies in the communication field is increasing. So the technologies such as modulation, transmission, classification are developing toward the orientation of better performance and more simple system. Modulation classification is one of the key technologies in communication system. It's an important issue, which have many scholars have been studying for decades. There have already been lots of different modulation classification algorithms, which fall into two categories:decision theoretic and statistical pattern recognition. Decision theoretic is based on the theory of probability judgment, stochastic processes and so on. This algorithm requires a lot of tedious knowledge which is hard to get access to. Consequently, the statistical pattern recognition has become a mainstream in recent years.
In order to reduce the complexity of the system and improve the performance, this dissertation bases on the analysis of previous works in the theory of statistical pattern recognition, researches the modulation classification of digital signal on the basis of OFDM system, and focuses on the modulated signal of ASK, FSK, PSK, and specially focus on MPSK. In this dissertation, we abstract some features to classify these signals and prove the performance of OFDM system. And the simulation results are also given in some chapters and sections.
Our main research results can be summarized mainly as follows:
1. The dissertation gives a detailed analysis of OFDM system and takes QPSK as an example, sets the transmit number of sub-carriers as128, and simulate the performance of transmission system to verify the system's high spectrum efficiency and good anti-noise performance.
2. Based on spectral analysis, this dissertation extracts two features as the parameter to identify ASK, FSK, PSK from each other, the two features are the number of spectral peaks as m, and the normalized spectrum average energy as H. We draw a flowcharts of modulation classification process and simulate the performance of two features and also the performance of the recognition algorithm as well.
3. With the modulation of MPSK based on OFDM channel system, the deduction and analysis of the second-order cumulants and the fourth-order cumulants of MPSK signal, An innovative feature to distinguish the modulation order of the MPSK is proposed. And this feature, due to its simple extracted approach and easy realization, can efficiently achieve the goal of modulation classification. This dissertation makes a comparison of the simulation of the MPSK recognition rate. The modulation algorithm is based on high-order cumulants. And the result shows that the advantage of high-order cumulants analysis is verified.
引文
[1]Fabrizi P.M., Lopes L.B., Lockhart G.B.. Receiver recognition of analogue modulation types. IEEE Conference on Radio Receiver and Associated Systems, Bangor, Wales,1986:135-140.
[2]Gardner W.A. The spectral correlation theory of cyclostationary time-series. Signal Processing,1986,11(1):13-36.
[3]Gardner W.A. Spectral correlation of modulated signals part I-Analogue modulation. IEEE Transactions on Communications,1987,35(6):584-594.
[4]Marchand P., Martret C. Le, Lacoume J-L., Classification of Linear Modulations by a Combination of Different Orders Cyclic Cumulants, Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics,1997,7(1):47-51.
[5]Gardner W,A, Brown W.A, Chen C.K.. Spectral correlation of modulated signals part II-Digital modulation. IEEE Transactions on Communications,1987,35(6):595-601.
[6]Gardner W.A, Spooner C.S., The Cumulant Theory of Cyclostationary Time-Series, Part I: Foundation, IEEE Trans on Signal Processing,1994,10(42):3387-3408.
[7]Gardner W.A., Spooner C.M. Cyclic spectral analysis for signal detection and modulation recognition. IEEE Military Communications Conference, San Diego, CA, USA,1988,1(2): 419-424.
[8]Muquet B., Wang Z., Giannakis G.B., Courville M.de. Cyclic Prefixing or Zero Padding for Multicarrier Transmissions, Communications, IEEE Trans on,2002,10(12):2136-2148.
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[11]Fu Haitao, Wan Qun, Shi Rong. Modulation Classification Based on Cyclic Spectral Features for Co-Channel Time-Frequency Overlapped Two-Signal. Pacific-Asia Conference on Circuits, Communications and Systems, Chengdu, China,2009,4(7):31-34.
[12]孟玲玲,李静.基于循环谱相关方法的MFSK信号识别.无线电通信技术,2010,36(1)：22-25.
[13]Nandi A. K., Azzouz E. E.. Automatic analogue modulation recognition. Signal Processing, 1995,46(2):211-222.
[14]AzzouzE. E., Nandi A K. Automatic identification of digital modulation types. Signal Processing,1995,47(1):55-69.
[15]Mirmomeni M., Moshiri B., Lucas C.. Modulation identification using combined classifiers And co-evolution of classifiers and tests.Proc.10th International Conference on Information Fusion,2007:1-8
[16]Nolan K, Doyle L, Mahony D., Mackenzle P.. Modulation scheme recognition techniques for software radio on a general Purpose Processor Platform. Proceedings of the First Joint IEI/IEE Symposium on Telecommunication Systems, Dublin,2001.
[17]Azzouz E. E, Nandi A. K.. Automatic Modulation Recognition of Communication Signals. Kluwer Academic Publishers, Netherlands,1996.
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[21]Nandi A. K., Azzouz E. E.. Algorithms for automatic modulation recognition of communication signals. IEEE Transactions on Communications,1998,46(4):431-436.
[22]Cho V, Prokopiw W., Chann Y.T., Identification of M-ary PSK anf FSK signals by the wavelet transform in Proceedings, IEEE Military Communications conf,1995,11(1) 886-890.
[23]李明宴,张鲁筠,江铭炎,许建华,张超.复杂脉内调制雷达信号的识别方法.计算机工程与应用,2011,47(15)：156-159.
[24]范海波,杨志俊,曹志刚.卫星通信常用调制方式的自动识别.通信学报,2004,25(1)：140-149.
[25]杨琳,许小东,路友荣,戴旭初,徐佩霞.基于谱线特征的恒包络数字调制方式识别方法.中国科学技术大学学报,2009,39(9)：936-943.
[26]Wagstaff A.J.. Logarithmic cyclic frequency domain profile for automatic modulation recognition[J]. IET Communications,2008,2(8):1009-1015.
[27]Fehske A., Gaeddert J., Reed J.H.. A new approach to signal classification using spectral correlation and neural networks. IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, MD, United states,2005(1):144-150.
[28]韩国栋,蔡斌,邬江兴.调制分析与识别的谱相关方法.系统工程与电子技术,2001,23(3)：34-36.
[29]朱雷,程汉文,吴乐南.利用循环谱和参数统计的数字调制信号识别.应用科学学报,2009,27(2)：137-143
[30]王瑛,程汉文,吴乐南.基于谱相关特征的信号调制方式识别.信息技术,2006,28(12)：25-28.
[31]Wu Hsiao-Chun, Saquib M., Yun Zhifeng. Novel Automatic Modulation Classification Using Cumulant Features for Communications via Multipath Channels. IEEE Transactions on Wireless Communications,2008,70(8):3098-3105.
[32]Han Gang, Li Jiandong, Lu Donghua. Study of modulation recognition based on HOCs and SVM. IEEE 59th Vehicular Technology Conference, Milan, Italy,2004(2):898-902.
[33]Dobre.O.A, Bar-Ness.Y, Wei Su. Higher-order cyclic cumulants for high order modulation classification,2003,10(13):112-117.
[34]Li Peng, Wang Fuping, Wang Zanji. Algorithm for Modulation Recognition Based on High-order Cumulants and Subspace Decomposition.8th International Conference on Signal Processing, Guilin, China,2006.
[35]顾学迈,丁楠.卫星通信常用数字调制方式自动识别算法研究.哈尔滨工业大学学报,2007,39(1)：93-94.
[36]Akmouche W.. Detection of multicarrier modulations using 4th-order cumulants. Proc of IEEE MILCOM. Atlantic City,NJ,USA:IEEE,1999(1):432-436.
[37]Weaver C.S.,Cole C.A.,Krumland R.B., Miller M.L., The Automatic Classification of Modulation Types by Pattern Recognition, Stanford Electronics Laboratories, Technical Report,1969,4(l):1829-1833.
[38]Mirmomeni M., Moshiri B.,Lucas C.. Modulation identification using combined classifiers And co-evolution of classifiers and tests.Proc.10th International Conference on Information Fusion,2007(1):1-8.
[39]Nolan K, Doyle L, Mahony D., Mackenzie P..Modulation scheme recognition techniques for software radio on a general Purpose Processor Platform. Proceedings of the First Joint IEI/IEE Symposium on Telecommunication Systems, Dublin,2001.
[40]Grimaldi D.,Rapuano S.,Truglia G. An automatic digital modulation classifier for measurement on telecommunication networks. Proc of IMTC2002. Anchrage, AK, USA: IEEE,2002(1):957-962.
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[2]Gardner W.A. The spectral correlation theory of cyclostationary time-series. Signal Processing,1986,11(1):13-36.
[3]Gardner W.A. Spectral correlation of modulated signals part I-Analogue modulation. IEEE Transactions on Communications,1987,35(6):584-594.
[4]Marchand P., Martret C. Le, Lacoume J-L., Classification of Linear Modulations by a Combination of Different Orders Cyclic Cumulants, Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics,1997,7(1):47-51.
[5]Gardner W,A, Brown W.A, Chen C.K.. Spectral correlation of modulated signals part II-Digital modulation. IEEE Transactions on Communications,1987,35(6):595-601.
[6]Gardner W.A, Spooner C.S., The Cumulant Theory of Cyclostationary Time-Series, Part I: Foundation, IEEE Trans on Signal Processing,1994,10(42):3387-3408.
[7]Gardner W.A., Spooner C.M. Cyclic spectral analysis for signal detection and modulation recognition. IEEE Military Communications Conference, San Diego, CA, USA,1988,1(2): 419-424.
[8]Muquet B., Wang Z., Giannakis G.B., Courville M.de. Cyclic Prefixing or Zero Padding for Multicarrier Transmissions, Communications, IEEE Trans on,2002,10(12):2136-2148.
[9]樊同亮OFDM系统的信道估计和信号均衡技术的研究.[博士学位论文].重庆.重庆大学.2012.
[10]孟玲玲,李静.基于循环谱相关方法的MFSK信号识别.无线电通信技术,2010,36(1)：22-25.
[11]Fu Haitao, Wan Qun, Shi Rong. Modulation Classification Based on Cyclic Spectral Features for Co-Channel Time-Frequency Overlapped Two-Signal. Pacific-Asia Conference on Circuits, Communications and Systems, Chengdu, China,2009,4(7):31-34.
[12]孟玲玲,李静.基于循环谱相关方法的MFSK信号识别.无线电通信技术,2010,36(1)：22-25.
[13]Nandi A. K., Azzouz E. E.. Automatic analogue modulation recognition. Signal Processing, 1995,46(2):211-222.
[14]AzzouzE. E., Nandi A K. Automatic identification of digital modulation types. Signal Processing,1995,47(1):55-69.
[15]Mirmomeni M., Moshiri B., Lucas C.. Modulation identification using combined classifiers And co-evolution of classifiers and tests.Proc.10th International Conference on Information Fusion,2007:1-8
[16]Nolan K, Doyle L, Mahony D., Mackenzle P.. Modulation scheme recognition techniques for software radio on a general Purpose Processor Platform. Proceedings of the First Joint IEI/IEE Symposium on Telecommunication Systems, Dublin,2001.
[17]Azzouz E. E, Nandi A. K.. Automatic Modulation Recognition of Communication Signals. Kluwer Academic Publishers, Netherlands,1996.
[18]Tadaion AA, Derakhtian M, Gazor S, Aref MR. Likelihood ratio test for PSK modulation classification in unkown noise environment. An adian Conference on Electrical and Computer Engineering.2005:151-154.
[19]陈卫东.数字通信信号调制识别算法研究.[博士学位论文].西安.西安电子科技大学.2001
[20]Nandi A. K., Azzouz E. E.. Modulation recognition using artificial neural networks. Signal Processing,1997,56(2):165-175.
[21]Nandi A. K., Azzouz E. E.. Algorithms for automatic modulation recognition of communication signals. IEEE Transactions on Communications,1998,46(4):431-436.
[22]Cho V, Prokopiw W., Chann Y.T., Identification of M-ary PSK anf FSK signals by the wavelet transform in Proceedings, IEEE Military Communications conf,1995,11(1) 886-890.
[23]李明宴,张鲁筠,江铭炎,许建华,张超.复杂脉内调制雷达信号的识别方法.计算机工程与应用,2011,47(15)：156-159.
[24]范海波,杨志俊,曹志刚.卫星通信常用调制方式的自动识别.通信学报,2004,25(1)：140-149.
[25]杨琳,许小东,路友荣,戴旭初,徐佩霞.基于谱线特征的恒包络数字调制方式识别方法.中国科学技术大学学报,2009,39(9)：936-943.
[26]Wagstaff A.J.. Logarithmic cyclic frequency domain profile for automatic modulation recognition[J]. IET Communications,2008,2(8):1009-1015.
[27]Fehske A., Gaeddert J., Reed J.H.. A new approach to signal classification using spectral correlation and neural networks. IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, MD, United states,2005(1):144-150.
[28]韩国栋,蔡斌,邬江兴.调制分析与识别的谱相关方法.系统工程与电子技术,2001,23(3)：34-36.
[29]朱雷,程汉文,吴乐南.利用循环谱和参数统计的数字调制信号识别.应用科学学报,2009,27(2)：137-143
[30]王瑛,程汉文,吴乐南.基于谱相关特征的信号调制方式识别.信息技术,2006,28(12)：25-28.
[31]Wu Hsiao-Chun, Saquib M., Yun Zhifeng. Novel Automatic Modulation Classification Using Cumulant Features for Communications via Multipath Channels. IEEE Transactions on Wireless Communications,2008,70(8):3098-3105.
[32]Han Gang, Li Jiandong, Lu Donghua. Study of modulation recognition based on HOCs and SVM. IEEE 59th Vehicular Technology Conference, Milan, Italy,2004(2):898-902.
[33]Dobre.O.A, Bar-Ness.Y, Wei Su. Higher-order cyclic cumulants for high order modulation classification,2003,10(13):112-117.
[34]Li Peng, Wang Fuping, Wang Zanji. Algorithm for Modulation Recognition Based on High-order Cumulants and Subspace Decomposition.8th International Conference on Signal Processing, Guilin, China,2006.
[35]顾学迈,丁楠.卫星通信常用数字调制方式自动识别算法研究.哈尔滨工业大学学报,2007,39(1)：93-94.
[36]Akmouche W.. Detection of multicarrier modulations using 4th-order cumulants. Proc of IEEE MILCOM. Atlantic City,NJ,USA:IEEE,1999(1):432-436.
[37]Weaver C.S.,Cole C.A.,Krumland R.B., Miller M.L., The Automatic Classification of Modulation Types by Pattern Recognition, Stanford Electronics Laboratories, Technical Report,1969,4(l):1829-1833.
[38]Mirmomeni M., Moshiri B.,Lucas C.. Modulation identification using combined classifiers And co-evolution of classifiers and tests.Proc.10th International Conference on Information Fusion,2007(1):1-8.
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