衰落信道下非合作接收中调制识别技术研究
|
摘要
调制识别技术广泛应用于各种场合,相关研究具有重要的现实意义和理论价值。虽然已经取得了很多成果,但随着信道环境日益复杂,各种新型调制方式的出现,仍有不少问题亟待解决。非合作衰落接收环境下,先验信息少,预处理、参数提取精度受限,现有方法识别效果有待提高。为了实现该环境下信号有效识别,本文开展衰落信道非合作接收环境中识别技术方面的研究,成果与主要贡献如下:
一、详细阐述了调制识别方面的研究进展,并按信道环境、预处理要求对已有调制识别方法进行归纳。对常见方法在识别性能、计算复杂度等方面进行比较,为各种条件下识别算法的选取提供参考。
二、建立衰落信道非合作接收处理模型,分析该模型下各种信号调制特征。研究的信号类型包含常见的单载波调制信号,以及正交频分复用(OFDM)和偏移正交幅度调制(OQAM)等多载波调制信号。提出的特征可由基带接收信号直接提取,受噪声和衰落的影响小。研究内容如下:
1、分析衰落信道下OFDM和OQAM基带信号谱相关特征,修正了OFDM和OQAM等多载波信号谱相关函数模型,证明了多载波信号的谱相关函数存在由调制序列自身周期性引起的循环平稳特征。
2、分析衰落信道下OFDM和OQAM基带信号的二阶循环平稳特征,提出并证明了信号的循环累量的相关表达式。
3、结合已有成果,分析常见单载波数字调制信号的循环平稳特征,提出并证明了单载波信号在衰落信道非合作接收处理模型下的相关结论。
三、针对衰落信道非合作接收中参数估计方面的问题,开展相关内容的研究。其中:
1、针对现有频域谱相关估计算法估计方差大,将数据加窗、重叠等处理方法引入频域估计算法,提出了一种改进的循环谱估计快速算法。与原方法相比,在估计性能不降低的条件下,该算法能有效降低估计方差。
2、提出一种零前缀OFDM信号参数盲估计算法,利用信号的二阶循环累积量特征,实现了对衰落信道下信号符号周期、零前缀长度等多个参数的联合估计。较已有方法,算法在频率选择性衰落信道下,估计特征更加稳健,能够实现调制参数有效估计。
3、利用衰落信道下OQAM信号二阶循环累积量和循环谱特征,提出了一种参数盲估计算法,实现了对OQAM信号符号周期、子载波数等参数的估计。通过实验进行验证,结果表明:算法能够在衰落信道盲接收条件下实现OQAM信号调制参数有效估计。
四、针对已有衰落环境下信号识别方法预处理要求高、识别种类有限、不适合非合作环境的问题,开展相关信号识别算法方面的研究。其中:
1、针对现有多载波信号识别方法识别种类有限,提出了一种改进的OFDM信号分类算法。扩展原算法多载波识别种类,实现在频率选择性衰落信道下,包括零前缀OFDM在内的多载波信号有效区分。
2、针对衰落信道下常见数字调制识别算法预处理要求高的问题,提出了一种衰落信道盲接收条件下的调制识别算法。该算法利用循环累积量的循环频率分布特征,结合循环平稳性检测,与已有算法相比,能够在无复杂预处理的条件下,更有效地实现衰落信道低信噪比接收环境下信号区分。
3、综合利用本文各种特征以及支持向量机(SVM)分类器,提出了基于SVM和循环平稳性检测的识别算法,进一步扩大分类集、提升识别效果。
全文最后介绍了课题相关工程实践情况,对工作进行了总结,指出尚存的不足,并对下一步研究进行了展望。
The modulation recognition technique, which is widely used in various occasions, has important applications and great theoretical values. Although a great progress have been made, there are still many key issues needed to be researched and solved, with the increasing complexity of channel and the appearance of new modulation schemes. The efficiency of existing classification methods need to improve under non-cooperative reception conditions in fading channel, with limited prior information and low precision of preprocessing. Aiming at the effective classification under non-cooperative reception conditions in fading channel, the modulation recognition technique are researched in this paper and the main contents and contributions are listed as follows.
The first part: progress of modulation classification research is indicated in detail. Existing modulation classification methods are summed up by channel conditions or pretreatment complexity. Common methods are analyzed from performance, computation complexity to pretreatment requirement, which provides reference to choosing algorithm under different conditions.
The second part: processing model of non-cooperative reception in fading channel is proposed, modulation characteristics under the model are analyzed. The modulation types include the common single carrier and multicarrier modulations, such as orthogonal frequency division multiplexing (OFDM) and offset quadrature amplitude modulation (OQAM). These characteristics, extracted directly from the received baseband signal, are hardly distortion to the fading channel and noise.
1. The spectral correlation functions (SCFs) of OFDM and OQAM baseband signal in fading channel are analyzed, SCF models of multicarrier signal such as OFDM and OQAM are corrected. The theoretical derivations confirm the SCF of multicarrier signal existing cyclostationarity induced by periodicity of the modulated sequences.
2. The second order cyclic cumulants of zero-padding OFDM (ZPOFDM) and OQAM baseband signal under fading channel are explored,cyclostationarity analyses are presented and expressions related to them are proposed and testified.
3. Cyclic cumulant characteristics of common digital modulated signals are analyzed, combined with the existing achievements. The related conclusions under non-cooperative reception fading channel model are proposed and proved.
The third part: the algorithms about parameter estimation under non-cooperative reception conditions in fading channel are proposed.
1. An improved fast algorithm is proposed for cyclic spectral estimation, considering the variance of original cyclic spectral estimation is unsatisfactory. Compared with the existing methods, the proposed algorithm brings the windowed overlapped data processing into the frequency smooth algorithm to improve the estimation quality, which decreases the requirement of data quantity without reducing the performance.
2. Based on the cyclic cumulant of ZPOFDM signals, a blind parameter estimation algorithm is proposed to estimate parameters such as symbol period, zero-padding time guard interval. Compared with the existing algorithm, the employed features of proposed algorithm is insensitive to the fading channels or the noise and lead to an efficient estimation in frequency selective channels.
3. A blind parameter estimation algorithm is proposed to estimate OQAM signal parameters such as symbol period, subcarrier number under the environment of fading channel, which exploited the second order cyclic cumulant and SCF of OQAM signal. The estimated performance is validated by experimental results, which indicate that the algorithm can achieve an efficient estimation under blind reception environments in the fading channel.
The fourth part: aiming at solving the problems of the existing signal recognition methods under fading conditions, such as high demand of pretreatment, limited recognizable modulation formats that are unsuitable to non-cooperation situation, some other recognition methods are proposed as follow.
1. For the limited recognizable modulation formats of the existing multicarrier signal classification methods, an improved OFDM signal recognition algorithm is put forward. This method has widened the recognition set with the ZPOFDM signal, and the recognition performance of the multicarrier signals in frequency selective fading channel is improved.
2. An algorithm is proposed in order to overcome the problem of high pretreatment requirements when classifying common digital modulated signals under non-cooperative reception conditions in fading channel. The algorithm combines cyclostationary detection with cyclic frequency distribution characteristics of cyclic cumulant, which accomplishes effectively recognition of common digital modulated signals.
3. A classification algorithm based on support vector machine (SVM) and cyclostationary properties is proposed after synthesizing the features and SVM classifiers, which can widen the recognition set and improve the classification effect.
Finally the related engineering implementations are also presented. The whole work of the dissertation is summarized, the shortcomings of the researches are pointed, and some suggestions on future researches are given.
一、详细阐述了调制识别方面的研究进展,并按信道环境、预处理要求对已有调制识别方法进行归纳。对常见方法在识别性能、计算复杂度等方面进行比较,为各种条件下识别算法的选取提供参考。
二、建立衰落信道非合作接收处理模型,分析该模型下各种信号调制特征。研究的信号类型包含常见的单载波调制信号,以及正交频分复用(OFDM)和偏移正交幅度调制(OQAM)等多载波调制信号。提出的特征可由基带接收信号直接提取,受噪声和衰落的影响小。研究内容如下:
1、分析衰落信道下OFDM和OQAM基带信号谱相关特征,修正了OFDM和OQAM等多载波信号谱相关函数模型,证明了多载波信号的谱相关函数存在由调制序列自身周期性引起的循环平稳特征。
2、分析衰落信道下OFDM和OQAM基带信号的二阶循环平稳特征,提出并证明了信号的循环累量的相关表达式。
3、结合已有成果,分析常见单载波数字调制信号的循环平稳特征,提出并证明了单载波信号在衰落信道非合作接收处理模型下的相关结论。
三、针对衰落信道非合作接收中参数估计方面的问题,开展相关内容的研究。其中:
1、针对现有频域谱相关估计算法估计方差大,将数据加窗、重叠等处理方法引入频域估计算法,提出了一种改进的循环谱估计快速算法。与原方法相比,在估计性能不降低的条件下,该算法能有效降低估计方差。
2、提出一种零前缀OFDM信号参数盲估计算法,利用信号的二阶循环累积量特征,实现了对衰落信道下信号符号周期、零前缀长度等多个参数的联合估计。较已有方法,算法在频率选择性衰落信道下,估计特征更加稳健,能够实现调制参数有效估计。
3、利用衰落信道下OQAM信号二阶循环累积量和循环谱特征,提出了一种参数盲估计算法,实现了对OQAM信号符号周期、子载波数等参数的估计。通过实验进行验证,结果表明:算法能够在衰落信道盲接收条件下实现OQAM信号调制参数有效估计。
四、针对已有衰落环境下信号识别方法预处理要求高、识别种类有限、不适合非合作环境的问题,开展相关信号识别算法方面的研究。其中:
1、针对现有多载波信号识别方法识别种类有限,提出了一种改进的OFDM信号分类算法。扩展原算法多载波识别种类,实现在频率选择性衰落信道下,包括零前缀OFDM在内的多载波信号有效区分。
2、针对衰落信道下常见数字调制识别算法预处理要求高的问题,提出了一种衰落信道盲接收条件下的调制识别算法。该算法利用循环累积量的循环频率分布特征,结合循环平稳性检测,与已有算法相比,能够在无复杂预处理的条件下,更有效地实现衰落信道低信噪比接收环境下信号区分。
3、综合利用本文各种特征以及支持向量机(SVM)分类器,提出了基于SVM和循环平稳性检测的识别算法,进一步扩大分类集、提升识别效果。
全文最后介绍了课题相关工程实践情况,对工作进行了总结,指出尚存的不足,并对下一步研究进行了展望。
The modulation recognition technique, which is widely used in various occasions, has important applications and great theoretical values. Although a great progress have been made, there are still many key issues needed to be researched and solved, with the increasing complexity of channel and the appearance of new modulation schemes. The efficiency of existing classification methods need to improve under non-cooperative reception conditions in fading channel, with limited prior information and low precision of preprocessing. Aiming at the effective classification under non-cooperative reception conditions in fading channel, the modulation recognition technique are researched in this paper and the main contents and contributions are listed as follows.
The first part: progress of modulation classification research is indicated in detail. Existing modulation classification methods are summed up by channel conditions or pretreatment complexity. Common methods are analyzed from performance, computation complexity to pretreatment requirement, which provides reference to choosing algorithm under different conditions.
The second part: processing model of non-cooperative reception in fading channel is proposed, modulation characteristics under the model are analyzed. The modulation types include the common single carrier and multicarrier modulations, such as orthogonal frequency division multiplexing (OFDM) and offset quadrature amplitude modulation (OQAM). These characteristics, extracted directly from the received baseband signal, are hardly distortion to the fading channel and noise.
1. The spectral correlation functions (SCFs) of OFDM and OQAM baseband signal in fading channel are analyzed, SCF models of multicarrier signal such as OFDM and OQAM are corrected. The theoretical derivations confirm the SCF of multicarrier signal existing cyclostationarity induced by periodicity of the modulated sequences.
2. The second order cyclic cumulants of zero-padding OFDM (ZPOFDM) and OQAM baseband signal under fading channel are explored,cyclostationarity analyses are presented and expressions related to them are proposed and testified.
3. Cyclic cumulant characteristics of common digital modulated signals are analyzed, combined with the existing achievements. The related conclusions under non-cooperative reception fading channel model are proposed and proved.
The third part: the algorithms about parameter estimation under non-cooperative reception conditions in fading channel are proposed.
1. An improved fast algorithm is proposed for cyclic spectral estimation, considering the variance of original cyclic spectral estimation is unsatisfactory. Compared with the existing methods, the proposed algorithm brings the windowed overlapped data processing into the frequency smooth algorithm to improve the estimation quality, which decreases the requirement of data quantity without reducing the performance.
2. Based on the cyclic cumulant of ZPOFDM signals, a blind parameter estimation algorithm is proposed to estimate parameters such as symbol period, zero-padding time guard interval. Compared with the existing algorithm, the employed features of proposed algorithm is insensitive to the fading channels or the noise and lead to an efficient estimation in frequency selective channels.
3. A blind parameter estimation algorithm is proposed to estimate OQAM signal parameters such as symbol period, subcarrier number under the environment of fading channel, which exploited the second order cyclic cumulant and SCF of OQAM signal. The estimated performance is validated by experimental results, which indicate that the algorithm can achieve an efficient estimation under blind reception environments in the fading channel.
The fourth part: aiming at solving the problems of the existing signal recognition methods under fading conditions, such as high demand of pretreatment, limited recognizable modulation formats that are unsuitable to non-cooperation situation, some other recognition methods are proposed as follow.
1. For the limited recognizable modulation formats of the existing multicarrier signal classification methods, an improved OFDM signal recognition algorithm is put forward. This method has widened the recognition set with the ZPOFDM signal, and the recognition performance of the multicarrier signals in frequency selective fading channel is improved.
2. An algorithm is proposed in order to overcome the problem of high pretreatment requirements when classifying common digital modulated signals under non-cooperative reception conditions in fading channel. The algorithm combines cyclostationary detection with cyclic frequency distribution characteristics of cyclic cumulant, which accomplishes effectively recognition of common digital modulated signals.
3. A classification algorithm based on support vector machine (SVM) and cyclostationary properties is proposed after synthesizing the features and SVM classifiers, which can widen the recognition set and improve the classification effect.
Finally the related engineering implementations are also presented. The whole work of the dissertation is summarized, the shortcomings of the researches are pointed, and some suggestions on future researches are given.
引文
[1]雷厉,石星,吕泽均,梁德文.侦察与监视[M].北京:国防工业出版社, 2008.
[2] O. A. Dobre, A. Abdi, Y. Bar-Ness, W. Su.Survey of automatic modulation classification techniques: classical approaches and new trends[J]. IET Communications,2007, 1(2):137-156.
[3] B. Ramkumar.Automatic modulation classification for cognitive radios using cyclic feature detection[J]. IEEE Circuits and Systems Magzine,2009, 2009(2):27-45.
[4] H. Wu, M. Saquib, Z. Yun.Novel automatic modulation classification using cumulant features for communications via multipath channels[J]. IEEE Transactions on Wireless Communications 2008, 7(8):3098-3105.
[5] J. Mitola. An integrated agent architecture for software defined radio[D]. Stockholm:Royal Institute of Technology Doctor thesis, 2000.
[6] C. S. Weaver, C. C. A, The automatic classification of modulation types by pattern recognition,to AD691069, 1969.
[7] W. A. Gardner.The spectral correlation theory of cyclostationary time-series[J]. Signal Processing,1986, 11(1):13-36.
[8] W. A. Gardner.Spectral correlation of modulated signals: Part I--Analog modulation[J]. IEEE Transactions on Communications,1987, 35(6):584-594.
[9] W. A. Gardner, W. Brown, C. Chen.Spectral correlation of modulated signals: Part II--Digital Modulation[J]. IEEE Transactions on Communications,1987, 35(6):595-601.
[10] W. A. Gardner.A unifying view of second-order measures of quality for signal classification[J]. IEEE Transactions on Communications,1980, 28(6):807-816.
[11] A. Polydoros, K. Kim.On the detection and classification of quadrature digital modulations in broad-band noise[J]. IEEE Transactions on Communications,1990, 38(2):1199-1211.
[12] C. Y. Huang, A. Polydoros.Likelihood methods for MPSK modulation classification[J]. IEEE Transactions on Communications,1995, 43(234):1493-1504.
[13] C. S. Long, K. M. Chugg, A. Polydoros.Further results in likelihood classification of QAM signals[A]. MILCOM' 94[C],1994:57-61.
[14] N. Ghani, L. R.Neural applied to the classification of spectral features for automatic modulation recognition[A]. MILCOM[C],1993:111-115.
[15] N. Lay, A. Polydoros.Modulation classification of signals in unknown ISI environments[A]. MICOM'95[C],1995:170-174.
[16] E. E. Azzouz, A. K. Nandi.Automatic Modulation Recognition of Communication Signals[M]. Kluwer: Academic Publishers, 1996.
[17] A. K. Nandi, E. E. Azzouz.Modulation recognition using artificial neural network[J]. Signal processing,1997, 56(2):165-175.
[18] A. K. Nandi, E. E. Azzouz.Algorithms for automatic modulation recognition ofcommunication signals[J]. IEEE Transactions on communications,1998, 46(4):431-436.
[19] C. J. Martret, D. M. Boiteau.Modulation classification by means of different orders statistical moments[A]. MICOM'97[C],1997:1387-1391.
[20] H. Xiaoming, D. Donoho.A simple and robust modulation classification method via counting[A]. ICASSP[C],1998:1-4.
[21] D. Donoho, H. Xiaoming.Large-sample modulation classification using Hellinger representation[A]. SPAWC[C],1997:1-5.
[22] A. O. Hero.Digital modulation classification using power moment matrices[A]. ICASSP[C],1998:311-315.
[23] H. H. Mahram.Robust QAM modulation classification via moment matrices[A]. PIMRC[C],2000:648-651.
[24] A. Swami, B. Sadler.Hierarchical digital modulation classification using cumulants[J]. IEEE Transactions on Communications,2000, 48(3):416-429.
[25] K. C. Ho, Prokopiw.Modulation identification of digital signals by the wavelet transform[A]. Radar Sonar and Navigation[C],2000:1-4.
[26] W. Wei, J. Mendel.Maximum-likelihood classification for digital amplitude-phase modulations[J]. IEEE Transactions on Communications,2002, 48(2):189-193.
[27] P. Panagiotou, A. Anastasopoulos, A. Polydoros.Likelihood ratio tests for modulation classification[A]. MILCOM[C],2000:670-674.
[28] O. A. Dobre, Y. Bar-Ness, W. Su.Higher Order Cyclic Cumulants for High Order Modulation Classification[A]. MILCOM[C],2003:112-117.
[29] O. A. Dobre, Y. Bar-Ness, W. Su.Robust QAM modulation classification algorithm using cyclic cumulants[A]. WCNC[C],2004:745-748.
[30] O. A. Dobre, A. Abdi, Y. Bar-Ness, W. Su.Cyclostationarity based modulation classification of linear digital modulations in flat fading channels[J]. Wireless Personal Communications,2010, 2010(54):699-717.
[31] F. Hameed, O. A. Dobre, D. Popescu.On the likelihood-based approach to modulation classification[J]. IEEE Transactions on Wireless Communications,2009, 8(12):5884-5892.
[32] V. D. Orlic, M. L. Dukic.Automatic modulation classification algorithm using higher-order cumulants under real-world channel conditions[J]. IEEE Communications Letters,2009, 13(12):917-919.
[33] A. Fehske, J. Gaeddert, J. Reed.A new approach to signal classification using spectral correlation and neural networks[A]. MICOM[C],2005:144-150.
[34] W. Headley, J. Reed, C. da Silva.Distributed cyclic spectrum feature-based modulation classification[A]. WCNC[C],2008:1200-1204.
[35] E. Like, D. Vasu.Signal classification in fading channels using cyclic spectral analysis[J]. EURASIP Journal on Wireless Communications and Networking,2009, 2009(1):1-14.
[36] O. A. Dobre, S. Rajan, R. Inkol.Joint signal detection and classification based on first-order cyclostationarity for cognitive radios[J]. EURASIP Journal on Advances in Signal Processing,2009, 2009(1):1-12.
[37]吕铁军.通信信号调制识别研究[D].成都:电子科技大学博士学位论文, 2000.
[38]陈卫东.数字通信信号调制识别算法研究[D].西安:西安电子科技大学博士学位论文, 2001.
[39]詹亚锋.通信信号自动制式识别及参数估计[D].北京:清华大学博士学位论文, 2004.
[40]罗明.数字通信信号的自动识别与参数估计研究[D].西安:西安电子科技大学博士论文, 2005.
[41]闻翔.通信信号调制识别技术研究[D].郑州:解放军信息工程大学博士学位论文, 2006.
[42]张炜.数字通信信号调制方式自动识别研究[D].长沙:国防科技大学博士学位论文, 2006.
[43]贺涛.数字通信信号调制识别若干新问题研究[D].成都:电子科技大学博士学位论文, 2007.
[44]高玉龙.基于循环谱的调制方式识别与高动态同步技术研究[D].哈尔滨:哈尔滨工业大学博士学位论文, 2007.
[45]王彬.无线衰落信道中的调制识别、信道盲辨识和盲均衡技术研究[D].郑州:解放军信息工程大学博士学位论文, 2007.
[46]刘双平.基于谐波检测与估计的线性调制分析方法研究[D].郑州:解放军信息工程大学博士学位论文, 2007.
[47]鲍丹.非理想条件下的调制分类问题[D].西安:西安电子科技大学博士学位论文, 2007.
[48]杨琳.数字通信信号调制方式自动识别技术研究[D].合肥:中国科学技术大学博士学位论文, 2008.
[49]陆明泉.多信号的调制识别技术研究[D].成都:电子科技大学博士学位论文, 2008.
[50]陈筱倩.基于广义相关分析的调制制式模糊分类研究[D].武汉:华中科技大学博士学位论文, 2009.
[51]赵树杰,赵建勋.信号检测与估计理论[M].北京:清华大学出版社, 2005.
[52] O. A. Dobre, F. Hameed. Likelihood based algorithms for linear digital modulation classification in fading channels[A]. Canadian Conference on Electrical and Computer Engineering[C],2006:1347-1350.
[53] W. Su, J. Xu, M. Zhou.Real-time modulation classification based on maximum likelihood[J]. IEEE Communications Letters,2008, 12(11):801-803.
[54] J. Hamkins, M. K. Simon.Autonomous Software-defined radio receivers for deep space applications[M]. New York: John Wiley and Sons, 2006.
[55] A. Abdi, O. Dobre, R. Choudhry, Y. Bar-Ness, W. Su.Modulation classification in fading channels using antenna arrays[A]. MILCOM[C],2005:211-217.
[56] L. Hong, K. Ho.Classification of BPSK and QPSK signals with unknown signal level using the Bayes technique[A]. 2003:
[57] A. El-Mahdy, N. Namazi.Classification of multiple M-ary frequency-shift keying signals over a Rayleigh fading channel[J]. IEEE Transactions on Communications,2002, 50(6):967-974.
[58] B. F. Beidas, C. L. Weber.Higher-order correlation-based approach to modulation classification of digitally frequency-modulated signals[J]. IEEE Journal on Selected Areas in Communications,1995, 13(10):89-101.
[59] B. F. Beidas, C. L. Weber.Asynchronous classification of MFSK signals using the higher order correlation domain[J]. IEEE Transactions on communications,1998, 46(3):480-493.
[60] O. Dobre, F. Hameed.On performance bounds for joint parameter estimation and modulation classification[A]. MILCOM[C],2008:1-5.
[61] H. Li, O. Dobre, Y. Bar-Ness, W. Su.Quasi-hybrid likelihood modulation classification with nonlinear carrier frequency offsets estimation using antenna arrays[A]. 2006:570-575.
[62] L. Hong.Maximum Likelihood BPSK and QPSK Classifier in Fading Environment using the EM algorithm[A]. 38th Southeastern Symposium on System Theory[C],2006:313-317.
[63]崔伟亮.复杂电磁背景下信号截获与分类[D].郑州:解放军信息工程大学硕士学位论文, 2007.
[64]钟骥.战场通信信号调制特征提取与监测分类[D].郑州:解放军信息工程大学硕士学位论文, 2008.
[65]张志民,欧建平,皇甫堪.基于一阶统计矩的数字通信信号调制方式的自动识别[J].信号处理,2010, 26(8):
[66]陈建文,葛临东,吴月娴.利用小波脊线实现数字调制信号的自动识别[J].电路与系统学报,2007, 12(3):73-77.
[67] A. Swami, B. Sadler.Modulation classification via hierarchical agglomerative cluster analysis [A]. SPAWC[C],1997:1-5.
[68] C. Weidong, Y. Shaoquan.Recursive classification of MQAM signals based on higher order cumulants [J]. JOURNAL OF ELECTRONICS(CHINA),2002, 19(3):417-422.
[69] S. Xi, H. Wu.Robust automatic modulation classification using cumulant features in the presence of fading channels[A]. WCNC 2006[C],2006:2094-2099.
[70] M. Shi, A. Laufer, Y. Bar-Ness, W. Su.Fourth order cumulants in distinguishing single carrier from OFDM signals[A]. IEEE MICOM[C],2008:1-6.
[71] W. Akmouche.Detection of multicarrier modulations using 4th-order cumulants[A]. MICOM[C],1999:432-436.
[72] C. M. Spooner.Classification of co-channel communication signals using cyclic cumulants[A]. ASILOMAR[C],1996:531-536.
[73]张贤达,保铮.非平稳信号分析与处理[M].北京:国防工业出版社, 1998.
[74]范海波,杨志俊,曹志刚.卫星通信常用调制方式的自动识别[J].通信学报,2004, 25(1):140-149.
[75]刘双平,闻翔,金梁.一种抑制符号速率估计背景色噪声的非线性滤波算法[J].电子学报,2007, 35(1):71-76.
[76]熊伟. VHF/UHF复合调制信号检测与参数估计[D].郑州:解放军信息工程大学硕士学位论文, 2009.
[77] W. Gardner.The spectral correlation theory of cyclostationary time-series[J]. Signal Processing,1986, 11(1):13-36.
[78] E. Like, V. Chakravarthy, Z. Wu.Reliable modulation classification at low SNR using spectral correlation[A]. CCNC[C],2007:1134-1138.
[79] E. Like, V. Chakravarthy, R. Husnay, Z. Wu.Modulation recognition in multipath fading channels using cyclic spectral analysis[A]. GLOBECOM[C],2008:1-6.
[80] E. Like. Modulation Recognition Via Exploitation of Cyclic Statistics[D]. Austin:Wright State University Master degree, 2007.
[81] B. Ramkumar, T. Bose, M. Radenkovic.Combined Blind Equalization and Automatic Modulation Classification for Cognitive Radios[A]. IEEE Proc[C],2009:172-177.
[82] O. A. Dobre, A. Punchihewa, S. Raj, R. Inkol.On the cyclostationarity of OFDM and single carrier linearly digitally modulated signals in time dispersive channels with applications to modulation recognition[A]. WCNC[C],2008:1284-1289.
[83] A. Punchihewa, Q. Zhang, O. A. Dobre, C. Spooner, S. Rajan, R. Inkol.On the cyclostationarity of OFDM and single carrier linearly digitally modulated signals in time dispersive channels: theoretical developments and application[J]. IEEE Transactions on Wireless Communications,2010, 9(8):2588-2599.
[84]吕铁军,郭双冰.调制信号的分形特征研究[J].中国科学(E辑),2001, 31(6):508-513.
[85] R. Hippenstiel, H. El-Kishky, P. Radev.On time-series analysis and signal classification part I: fractal dimensions[A]. IEEE Proc. SSC[C],2004:2121-2125.
[86]吕铁军,郭双冰.基于复杂度特征的调制信号识别[J].通信学报,2002, 23(1):111-115.
[87] D. Boutte, B. Santhanam.ISI effects in a hybrid ICA-SVM modulation recognition algorithm[A]. Asilomar 2008[C],2008:457-460.
[88] D. Boutte, B. Santhanam.A feature weighted hybrid ICA-SVM approach to automatic modulation recognition[A]. IEEE Proc[C],2009:399-403.
[89]景小荣.多天线通信系统中的信号检测与信道估计技术研究[D].成都:电子科大博士学位论文, 2009.
[90]陈芳炯.信道盲辨识、盲均衡理论及应用研究[D].广州:华南理工大学博士学位论文, 2002.
[91]朱文贵.基于阵列信号处理的短波跳频信号盲检测和参数盲估计[D].合肥:中国科学技术大学博士学位论文, 2007.
[92]李颖.平衰落信道下多天线系统中的信号检测算法研究[D].长沙:国防科技大学博士学位论文, 2006.
[93] V. K. Madisetti, D. B.Williams, G. B. Giannakis.The Digital Signal Processing Handbook[M]. Boca Raton: CRC Press, 1999.
[94] E. G. Gladysev.Periodically correlated random sequences[J]. Soviet Math,1961, 2(1):385-388.
[95] H. L. Hurd. An Investigation of Periodically Correlated Stochastic Processes[D]. Durham:Duke UniversityPh.D. Dissertation, 1969.
[96] R. S. Roberts, W. A. Brown, H. H. J. Loomis.Computationally efficient algorithms for cyclic spectral analysis[J]. IEEE Signal Processing Magazine,1991, 8(2):38-49.
[97] W. A. Gardner.Exploitation of spectral redundancy in cyclostationary signals[J]. IEEE Signal Processing Magazine,1991, 8(2):14-36.
[98] W. A. Gardner, C. M. Spooner.The cumulant theory of cyclostationary time-series, Part I -II[J]. IEEE Transactions on Signal Processing,1994, 42(12):3387-3428.
[99] J. Antoni.Cyclic spectral analysis in practice[J]. Mechanical Systems and Signal Processing,2007, 21(2):597-630.
[100] W. A. Gardner.cyclostationarity in communications and signal processing[M]. New York: IEEE Press, 1994.
[101] W. A. Gardner, A. Napolitano, L. Paura.Cyclostationarity: half a century of research[J]. Signal Processing,2006, 86(4):639-697.
[102] O. A. Dobre, S. Rajan, R. Inkol.Exploitation of first-order cyclostationarity for joint signal detection and classification in cognitive radio[A]. IEEE Proc[C],2008:1-5.
[103] J. G. Proakis.Digital communications (4th edition)[M]. New York: McGraw-Hill, 2001.
[104]徐明远,陈德章,冯云.无线电信号频谱分析[M].北京:科学出版社, 2008.
[105] P. Siohan, C. Siclet, N. Lacaille.Analysis and design of OFDM/OQAM systems based on filterbank theory[J]. IEEE Transactions on Signal Processing,2002, 50(5):1170-1183.
[106] S. B. Weinstein.The history of OFDM[J]. IEEE Communications Magazine,2009, 2009(12):26-35.
[107] B. Muquet, Z. Wang, G. B. Giannakis, M. d. Courville, P. Duhamel.Cyclic Prefixing or Zero Padding for Wireless Multicarrier Transmissions?[J]. IEEE Transactions on Communications,2002, 50(12):2136-2148.
[108] R. Peled, A. Ruiz.Frequency Domain Data Transmission using Reduced Computational Complexity Algorithms[A]. IEEE Acoustics, Speech, and Signal Processing[C],1980:964-967.
[109]黄海,李长青,温志刚,彭端,尹长川,乐光新.一种零前缀OFDM系统的符号同步和载频估计算法[J].电路与系统学报,2009, 14(3):82-86.
[110] N. Han, G. Zheng, S. H. Sohn, J. M. Kim.Cyclic autocorrelation based blind OFDM detection and identification for cognitive radio[A]. IEEE Proc[C],2008:1-5.
[111] O. A. Dobre, A. Punchihewa, S. Raj, R. Inkol.Cyclostationarity-based algorithm for blind recognition of OFDM and single carrier linear digital modulations[A]. PIMRC'07[C],2007:1-5.
[112] B. R. Saltzberg.Performance of an efficient parallel data transmission system[J]. IEEE Trans. Commun. Tech.,1967, 15(6):805-811.
[113]胡苏,武刚,肖悦,李少谦.OFDM/OQAM系统中联合迭代信道估计和信号检测[J].电子与信息学报,2009, 31(10):2332-2337.
[114] B. Hirosaki.An orthogonally multiplexed QAM system using the discrete Fouriertransform[J]. IEEE Transactions on Communications,1981, 29(7):982-989.
[115] L. Vangelista, N. Laurenti.Efficient implementations and alternative architectures for OFDM-OQAM systems[J]. IEEE Transactions on Communications,2001, 49(4):
[116]张贤达.现代信号处理[M].北京:清华大学出版社, 2002.
[117]戴幻尧,蒋鸿宇,李群.基于循环谱分析的LPI信号特征检测新方法研究[J].信号处理,2009, 25(5):781-786.
[118]黄春琳.基于循环平稳特性的低截获概率信号的截获技术研究[D].长沙:国防科技大学博士学位论文, 2001.
[119] H. Zhang, A. Abdi.Cyclostationarity-based doppler spread estimation in mobile fading channels[J]. IEEE Transactions on Communications,2009, 57(4):1061-1067.
[120]徐海源.雷达/通信侦察中相位编码信号分析处理技术研究[D].长沙:国防科技大学博士学位论文, 2007.
[121]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[J].应用科学学报,2009, 27(2):138-143.
[122] D. Vucic, M. Obradovic.Cyclic spectral analysis of phase-incoherent FSK signal by matrix-based stochastic method[A]. TELSIKS 2001[C],2001:267-270.
[123] D. Vucic.Cyclic spectral analysis of frequency hopping signals[A]. 16th International Symposium on Personal, Indoor and Mobile Radio Communications[C],2005:1209-1213.
[124] D. Vucic, M. Eric, I. Pokrajac.Cyclic spectral analysis of TH-PPM UWB impulse radio signals[A]. TELSIKS 2009[C],2009:202-205.
[125] D. Vucic, M. Eric.Cyclic Spectral Analysis of UWB-IR Signals[J]. IEEE Signal Processing Letters,2009, 16(8):723-726.
[126] D. Vucic, M. Obradovic, D. Obradovic.Spectral correlation of OFDM signals related to their PLC applications[A]. 6th International Symposium on Power-Line Communications and Its Applications[C],2002:1-4.
[127]黄奇珊,彭启琮,路友荣,韩猛.OFDM信号循环谱结构分析[J].电子与信息学报,2008, 30(1):134-138.
[128] H. Zhang, D. L. Ruyet, M. Terr′.Spectral Correlation of Multicarrier Modulated Signals and Its Application for Signal Detection[J]. EURASIP Journal on Advances in Signal Processing,2010, 2010(1):1-14.
[129] H. Bolcskei.Blind estimation of symbol timing and carrier frequency offset in wireless OFDM systems[J]. IEEE Transactions on Communications 2002, 49(6):988-999.
[130] P. D. Sutton, K. E. Nolan, L. E. Doyle.Cyclostationary signatures in practical cognitive radio applications[J]. IEEE Journal on Selected Areas in Communications,2008, 26(1):13-24.
[131] W. A. Gardner, N. Antonio, L. Paura.One-bit spectral-corrlation algorithms[J]. IEEE Transactions on Signal Processing,1993, 41(1):423-427.
[132]高玉龙.任意分辨率的SSCA算法研究[J].哈尔滨工业大学学报,2008, 40(7):1040-1043.
[133] A. V. Dandawate, G. B. Giannakis.Nonparametric polyspectral estimators for kth-order(almost) cyclostationary processes[J]. IEEE Transactions on Information Theory,1994, 40(1):67-84.
[134]史建锋,朱良学.有限数据下循环谱的频域平滑对称式周期图法估计性能分析[J].数据采集与处理,2004, 19(2):155-159.
[135]郑文秀.数字通信信号的参数估计与干扰技术研究[D].西安:西安电子科技大学博士论文, 2008.
[136]周家喜,许小东,徐佩霞,戴旭初.一种CPM信号成形脉冲盲估计算法[J].电子与信息学报2009, 31(11):2639-2642.
[137]郑文秀,赵国庆,罗明.基于高阶循环累积量的OFDM子载波盲估计[J].电子与信息学报,2008, 30(02):346-349.
[138] M. Shi, Y. Bar-Ness, W. Su.Blind OFDM systems parameters estimation for software defined radio[A]. NFDSAN 07[C],2007:119-122.
[139] A. Bouzegzi, P. Ciblat, P. Jallon.New algorithms for blind recognition of OFDM based systems[J]. Signal Processing,2010, 90(3):900-913.
[140] T. van Waterschoot, V. Le Nir, J. Duplicy, M. Moonen.Analytical expressions for the power spectral density of CP-OFDM and ZP-OFDM signals[J]. IEEE Signal Processing Letters,2010, 17(4):371-374.
[141] V. Le Nir, T. Van Waterschoot, M. Moonen, J. Duplicy.Blind CP-OFDM and ZP-OFDM parameter estimation in frequency selective channels[J]. EURASIP Journal on Wireless Communications and Networking,2009, 2009(1):1-10.
[142] H. Ishii, G. W. Wornell.OFDM blind parameter identification in cognitive radios[A]. IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communication[C],2005:700-705.
[143] S. Ma, X. Pan, G. Yang, T. Ng.Blind Symbol Synchronization Based on Cyclic Prefix for OFDM Systems[J]. IEEE Transactions on Vehicular Technology,2009, 58(4):1746-1751.
[144] J. Wang, T. Chen, B. Huang.Cyclo-period estimation for discrete-time cyclo-stationary signals[J]. IEEE Transactions on Signal Processing,2006, 54(1):83-94.
[145] H. L. Hurd, N. L. Gerr.Graphical methods for determining the presence of periodic correlation[J]. J. Times Ser. Anal.,1991, 12(4):337-350.
[146]D. E. K. Martin, B. Kedem.Estimation of the period of periodically correlated sequences[J]. J. Times Ser. Anal.,1993, 14(2):193-205.
[147] A. V. Dandawate, G. B. Giannakis.Statistical tests for presence of cyclostationarity[J]. IEEE Transactions on Signal Processing,2002, 42(9):2355-2369.
[148] H. Li, Y. Bar-Ness, A. Abdi, W. Su.OFDM modulation classification and parameters extraction[A]. IEEE CROWNCOM[C],2006:1-6.
[149] F. Liedtke, U. Albers.Evaluation of features for the automatic recognition of OFDM signals in monitoring or cognitive receivers[J]. Journal of telecommunications and information technology,2008, 2008(2):30-36.
[150]赵知劲,周云水,尚俊娜.基于时频分析和RBF神经网络的调制识别[J].压电与声光,2004, 26(4):328-330.
[151] C. Nello, S. T. John.支持向量机导论[M].北京:电子工业出版社, 2005.
[152] R. Proesch.Technical handbook for radio monitoring HF[M]. Norderstedt: Books on Demand Gmbh, 2009.
[153]周家喜.非协作通信中连续相位调制信号盲解调关键技术研究[D].合肥:中国科技大学博士学位论文, 2009.
[154] G. Cariolaro.A System-Theory Approach to Decompose CPM Signals into PAM Waveforms[J]. IEEE Transactions on communications,2010, 58(1):200-210.
[155]周家喜,许小东,徐佩霞,戴旭初.一种CPM信号频率成形脉冲盲估计算法[J].电子与信息学报,2009, 31(11):2637-2642.
[156]陆明泉.同信道多信号的调制识别方法[J].清华大学学报,2009, 49(10):103-107.
[157]程汉文.基于累计量的干扰信号调制识别算法[J].电子与信息学报,2009, 31(7):1741-1745.
[158] H. T. Fu.Modulation Classification Based on Cyclic Spectral Features for Co-Channel Time-Frequency Overlapped Two-Signal[A]. PACCS[C],2009:31-34.
[159]于志明,郭黎利,孙志国.多径信道下多载波调制信号盲识别算法[J].电子与信息学报,2010, 32(7):1756-1759.
[2] O. A. Dobre, A. Abdi, Y. Bar-Ness, W. Su.Survey of automatic modulation classification techniques: classical approaches and new trends[J]. IET Communications,2007, 1(2):137-156.
[3] B. Ramkumar.Automatic modulation classification for cognitive radios using cyclic feature detection[J]. IEEE Circuits and Systems Magzine,2009, 2009(2):27-45.
[4] H. Wu, M. Saquib, Z. Yun.Novel automatic modulation classification using cumulant features for communications via multipath channels[J]. IEEE Transactions on Wireless Communications 2008, 7(8):3098-3105.
[5] J. Mitola. An integrated agent architecture for software defined radio[D]. Stockholm:Royal Institute of Technology Doctor thesis, 2000.
[6] C. S. Weaver, C. C. A, The automatic classification of modulation types by pattern recognition,to AD691069, 1969.
[7] W. A. Gardner.The spectral correlation theory of cyclostationary time-series[J]. Signal Processing,1986, 11(1):13-36.
[8] W. A. Gardner.Spectral correlation of modulated signals: Part I--Analog modulation[J]. IEEE Transactions on Communications,1987, 35(6):584-594.
[9] W. A. Gardner, W. Brown, C. Chen.Spectral correlation of modulated signals: Part II--Digital Modulation[J]. IEEE Transactions on Communications,1987, 35(6):595-601.
[10] W. A. Gardner.A unifying view of second-order measures of quality for signal classification[J]. IEEE Transactions on Communications,1980, 28(6):807-816.
[11] A. Polydoros, K. Kim.On the detection and classification of quadrature digital modulations in broad-band noise[J]. IEEE Transactions on Communications,1990, 38(2):1199-1211.
[12] C. Y. Huang, A. Polydoros.Likelihood methods for MPSK modulation classification[J]. IEEE Transactions on Communications,1995, 43(234):1493-1504.
[13] C. S. Long, K. M. Chugg, A. Polydoros.Further results in likelihood classification of QAM signals[A]. MILCOM' 94[C],1994:57-61.
[14] N. Ghani, L. R.Neural applied to the classification of spectral features for automatic modulation recognition[A]. MILCOM[C],1993:111-115.
[15] N. Lay, A. Polydoros.Modulation classification of signals in unknown ISI environments[A]. MICOM'95[C],1995:170-174.
[16] E. E. Azzouz, A. K. Nandi.Automatic Modulation Recognition of Communication Signals[M]. Kluwer: Academic Publishers, 1996.
[17] A. K. Nandi, E. E. Azzouz.Modulation recognition using artificial neural network[J]. Signal processing,1997, 56(2):165-175.
[18] A. K. Nandi, E. E. Azzouz.Algorithms for automatic modulation recognition ofcommunication signals[J]. IEEE Transactions on communications,1998, 46(4):431-436.
[19] C. J. Martret, D. M. Boiteau.Modulation classification by means of different orders statistical moments[A]. MICOM'97[C],1997:1387-1391.
[20] H. Xiaoming, D. Donoho.A simple and robust modulation classification method via counting[A]. ICASSP[C],1998:1-4.
[21] D. Donoho, H. Xiaoming.Large-sample modulation classification using Hellinger representation[A]. SPAWC[C],1997:1-5.
[22] A. O. Hero.Digital modulation classification using power moment matrices[A]. ICASSP[C],1998:311-315.
[23] H. H. Mahram.Robust QAM modulation classification via moment matrices[A]. PIMRC[C],2000:648-651.
[24] A. Swami, B. Sadler.Hierarchical digital modulation classification using cumulants[J]. IEEE Transactions on Communications,2000, 48(3):416-429.
[25] K. C. Ho, Prokopiw.Modulation identification of digital signals by the wavelet transform[A]. Radar Sonar and Navigation[C],2000:1-4.
[26] W. Wei, J. Mendel.Maximum-likelihood classification for digital amplitude-phase modulations[J]. IEEE Transactions on Communications,2002, 48(2):189-193.
[27] P. Panagiotou, A. Anastasopoulos, A. Polydoros.Likelihood ratio tests for modulation classification[A]. MILCOM[C],2000:670-674.
[28] O. A. Dobre, Y. Bar-Ness, W. Su.Higher Order Cyclic Cumulants for High Order Modulation Classification[A]. MILCOM[C],2003:112-117.
[29] O. A. Dobre, Y. Bar-Ness, W. Su.Robust QAM modulation classification algorithm using cyclic cumulants[A]. WCNC[C],2004:745-748.
[30] O. A. Dobre, A. Abdi, Y. Bar-Ness, W. Su.Cyclostationarity based modulation classification of linear digital modulations in flat fading channels[J]. Wireless Personal Communications,2010, 2010(54):699-717.
[31] F. Hameed, O. A. Dobre, D. Popescu.On the likelihood-based approach to modulation classification[J]. IEEE Transactions on Wireless Communications,2009, 8(12):5884-5892.
[32] V. D. Orlic, M. L. Dukic.Automatic modulation classification algorithm using higher-order cumulants under real-world channel conditions[J]. IEEE Communications Letters,2009, 13(12):917-919.
[33] A. Fehske, J. Gaeddert, J. Reed.A new approach to signal classification using spectral correlation and neural networks[A]. MICOM[C],2005:144-150.
[34] W. Headley, J. Reed, C. da Silva.Distributed cyclic spectrum feature-based modulation classification[A]. WCNC[C],2008:1200-1204.
[35] E. Like, D. Vasu.Signal classification in fading channels using cyclic spectral analysis[J]. EURASIP Journal on Wireless Communications and Networking,2009, 2009(1):1-14.
[36] O. A. Dobre, S. Rajan, R. Inkol.Joint signal detection and classification based on first-order cyclostationarity for cognitive radios[J]. EURASIP Journal on Advances in Signal Processing,2009, 2009(1):1-12.
[37]吕铁军.通信信号调制识别研究[D].成都:电子科技大学博士学位论文, 2000.
[38]陈卫东.数字通信信号调制识别算法研究[D].西安:西安电子科技大学博士学位论文, 2001.
[39]詹亚锋.通信信号自动制式识别及参数估计[D].北京:清华大学博士学位论文, 2004.
[40]罗明.数字通信信号的自动识别与参数估计研究[D].西安:西安电子科技大学博士论文, 2005.
[41]闻翔.通信信号调制识别技术研究[D].郑州:解放军信息工程大学博士学位论文, 2006.
[42]张炜.数字通信信号调制方式自动识别研究[D].长沙:国防科技大学博士学位论文, 2006.
[43]贺涛.数字通信信号调制识别若干新问题研究[D].成都:电子科技大学博士学位论文, 2007.
[44]高玉龙.基于循环谱的调制方式识别与高动态同步技术研究[D].哈尔滨:哈尔滨工业大学博士学位论文, 2007.
[45]王彬.无线衰落信道中的调制识别、信道盲辨识和盲均衡技术研究[D].郑州:解放军信息工程大学博士学位论文, 2007.
[46]刘双平.基于谐波检测与估计的线性调制分析方法研究[D].郑州:解放军信息工程大学博士学位论文, 2007.
[47]鲍丹.非理想条件下的调制分类问题[D].西安:西安电子科技大学博士学位论文, 2007.
[48]杨琳.数字通信信号调制方式自动识别技术研究[D].合肥:中国科学技术大学博士学位论文, 2008.
[49]陆明泉.多信号的调制识别技术研究[D].成都:电子科技大学博士学位论文, 2008.
[50]陈筱倩.基于广义相关分析的调制制式模糊分类研究[D].武汉:华中科技大学博士学位论文, 2009.
[51]赵树杰,赵建勋.信号检测与估计理论[M].北京:清华大学出版社, 2005.
[52] O. A. Dobre, F. Hameed. Likelihood based algorithms for linear digital modulation classification in fading channels[A]. Canadian Conference on Electrical and Computer Engineering[C],2006:1347-1350.
[53] W. Su, J. Xu, M. Zhou.Real-time modulation classification based on maximum likelihood[J]. IEEE Communications Letters,2008, 12(11):801-803.
[54] J. Hamkins, M. K. Simon.Autonomous Software-defined radio receivers for deep space applications[M]. New York: John Wiley and Sons, 2006.
[55] A. Abdi, O. Dobre, R. Choudhry, Y. Bar-Ness, W. Su.Modulation classification in fading channels using antenna arrays[A]. MILCOM[C],2005:211-217.
[56] L. Hong, K. Ho.Classification of BPSK and QPSK signals with unknown signal level using the Bayes technique[A]. 2003:
[57] A. El-Mahdy, N. Namazi.Classification of multiple M-ary frequency-shift keying signals over a Rayleigh fading channel[J]. IEEE Transactions on Communications,2002, 50(6):967-974.
[58] B. F. Beidas, C. L. Weber.Higher-order correlation-based approach to modulation classification of digitally frequency-modulated signals[J]. IEEE Journal on Selected Areas in Communications,1995, 13(10):89-101.
[59] B. F. Beidas, C. L. Weber.Asynchronous classification of MFSK signals using the higher order correlation domain[J]. IEEE Transactions on communications,1998, 46(3):480-493.
[60] O. Dobre, F. Hameed.On performance bounds for joint parameter estimation and modulation classification[A]. MILCOM[C],2008:1-5.
[61] H. Li, O. Dobre, Y. Bar-Ness, W. Su.Quasi-hybrid likelihood modulation classification with nonlinear carrier frequency offsets estimation using antenna arrays[A]. 2006:570-575.
[62] L. Hong.Maximum Likelihood BPSK and QPSK Classifier in Fading Environment using the EM algorithm[A]. 38th Southeastern Symposium on System Theory[C],2006:313-317.
[63]崔伟亮.复杂电磁背景下信号截获与分类[D].郑州:解放军信息工程大学硕士学位论文, 2007.
[64]钟骥.战场通信信号调制特征提取与监测分类[D].郑州:解放军信息工程大学硕士学位论文, 2008.
[65]张志民,欧建平,皇甫堪.基于一阶统计矩的数字通信信号调制方式的自动识别[J].信号处理,2010, 26(8):
[66]陈建文,葛临东,吴月娴.利用小波脊线实现数字调制信号的自动识别[J].电路与系统学报,2007, 12(3):73-77.
[67] A. Swami, B. Sadler.Modulation classification via hierarchical agglomerative cluster analysis [A]. SPAWC[C],1997:1-5.
[68] C. Weidong, Y. Shaoquan.Recursive classification of MQAM signals based on higher order cumulants [J]. JOURNAL OF ELECTRONICS(CHINA),2002, 19(3):417-422.
[69] S. Xi, H. Wu.Robust automatic modulation classification using cumulant features in the presence of fading channels[A]. WCNC 2006[C],2006:2094-2099.
[70] M. Shi, A. Laufer, Y. Bar-Ness, W. Su.Fourth order cumulants in distinguishing single carrier from OFDM signals[A]. IEEE MICOM[C],2008:1-6.
[71] W. Akmouche.Detection of multicarrier modulations using 4th-order cumulants[A]. MICOM[C],1999:432-436.
[72] C. M. Spooner.Classification of co-channel communication signals using cyclic cumulants[A]. ASILOMAR[C],1996:531-536.
[73]张贤达,保铮.非平稳信号分析与处理[M].北京:国防工业出版社, 1998.
[74]范海波,杨志俊,曹志刚.卫星通信常用调制方式的自动识别[J].通信学报,2004, 25(1):140-149.
[75]刘双平,闻翔,金梁.一种抑制符号速率估计背景色噪声的非线性滤波算法[J].电子学报,2007, 35(1):71-76.
[76]熊伟. VHF/UHF复合调制信号检测与参数估计[D].郑州:解放军信息工程大学硕士学位论文, 2009.
[77] W. Gardner.The spectral correlation theory of cyclostationary time-series[J]. Signal Processing,1986, 11(1):13-36.
[78] E. Like, V. Chakravarthy, Z. Wu.Reliable modulation classification at low SNR using spectral correlation[A]. CCNC[C],2007:1134-1138.
[79] E. Like, V. Chakravarthy, R. Husnay, Z. Wu.Modulation recognition in multipath fading channels using cyclic spectral analysis[A]. GLOBECOM[C],2008:1-6.
[80] E. Like. Modulation Recognition Via Exploitation of Cyclic Statistics[D]. Austin:Wright State University Master degree, 2007.
[81] B. Ramkumar, T. Bose, M. Radenkovic.Combined Blind Equalization and Automatic Modulation Classification for Cognitive Radios[A]. IEEE Proc[C],2009:172-177.
[82] O. A. Dobre, A. Punchihewa, S. Raj, R. Inkol.On the cyclostationarity of OFDM and single carrier linearly digitally modulated signals in time dispersive channels with applications to modulation recognition[A]. WCNC[C],2008:1284-1289.
[83] A. Punchihewa, Q. Zhang, O. A. Dobre, C. Spooner, S. Rajan, R. Inkol.On the cyclostationarity of OFDM and single carrier linearly digitally modulated signals in time dispersive channels: theoretical developments and application[J]. IEEE Transactions on Wireless Communications,2010, 9(8):2588-2599.
[84]吕铁军,郭双冰.调制信号的分形特征研究[J].中国科学(E辑),2001, 31(6):508-513.
[85] R. Hippenstiel, H. El-Kishky, P. Radev.On time-series analysis and signal classification part I: fractal dimensions[A]. IEEE Proc. SSC[C],2004:2121-2125.
[86]吕铁军,郭双冰.基于复杂度特征的调制信号识别[J].通信学报,2002, 23(1):111-115.
[87] D. Boutte, B. Santhanam.ISI effects in a hybrid ICA-SVM modulation recognition algorithm[A]. Asilomar 2008[C],2008:457-460.
[88] D. Boutte, B. Santhanam.A feature weighted hybrid ICA-SVM approach to automatic modulation recognition[A]. IEEE Proc[C],2009:399-403.
[89]景小荣.多天线通信系统中的信号检测与信道估计技术研究[D].成都:电子科大博士学位论文, 2009.
[90]陈芳炯.信道盲辨识、盲均衡理论及应用研究[D].广州:华南理工大学博士学位论文, 2002.
[91]朱文贵.基于阵列信号处理的短波跳频信号盲检测和参数盲估计[D].合肥:中国科学技术大学博士学位论文, 2007.
[92]李颖.平衰落信道下多天线系统中的信号检测算法研究[D].长沙:国防科技大学博士学位论文, 2006.
[93] V. K. Madisetti, D. B.Williams, G. B. Giannakis.The Digital Signal Processing Handbook[M]. Boca Raton: CRC Press, 1999.
[94] E. G. Gladysev.Periodically correlated random sequences[J]. Soviet Math,1961, 2(1):385-388.
[95] H. L. Hurd. An Investigation of Periodically Correlated Stochastic Processes[D]. Durham:Duke UniversityPh.D. Dissertation, 1969.
[96] R. S. Roberts, W. A. Brown, H. H. J. Loomis.Computationally efficient algorithms for cyclic spectral analysis[J]. IEEE Signal Processing Magazine,1991, 8(2):38-49.
[97] W. A. Gardner.Exploitation of spectral redundancy in cyclostationary signals[J]. IEEE Signal Processing Magazine,1991, 8(2):14-36.
[98] W. A. Gardner, C. M. Spooner.The cumulant theory of cyclostationary time-series, Part I -II[J]. IEEE Transactions on Signal Processing,1994, 42(12):3387-3428.
[99] J. Antoni.Cyclic spectral analysis in practice[J]. Mechanical Systems and Signal Processing,2007, 21(2):597-630.
[100] W. A. Gardner.cyclostationarity in communications and signal processing[M]. New York: IEEE Press, 1994.
[101] W. A. Gardner, A. Napolitano, L. Paura.Cyclostationarity: half a century of research[J]. Signal Processing,2006, 86(4):639-697.
[102] O. A. Dobre, S. Rajan, R. Inkol.Exploitation of first-order cyclostationarity for joint signal detection and classification in cognitive radio[A]. IEEE Proc[C],2008:1-5.
[103] J. G. Proakis.Digital communications (4th edition)[M]. New York: McGraw-Hill, 2001.
[104]徐明远,陈德章,冯云.无线电信号频谱分析[M].北京:科学出版社, 2008.
[105] P. Siohan, C. Siclet, N. Lacaille.Analysis and design of OFDM/OQAM systems based on filterbank theory[J]. IEEE Transactions on Signal Processing,2002, 50(5):1170-1183.
[106] S. B. Weinstein.The history of OFDM[J]. IEEE Communications Magazine,2009, 2009(12):26-35.
[107] B. Muquet, Z. Wang, G. B. Giannakis, M. d. Courville, P. Duhamel.Cyclic Prefixing or Zero Padding for Wireless Multicarrier Transmissions?[J]. IEEE Transactions on Communications,2002, 50(12):2136-2148.
[108] R. Peled, A. Ruiz.Frequency Domain Data Transmission using Reduced Computational Complexity Algorithms[A]. IEEE Acoustics, Speech, and Signal Processing[C],1980:964-967.
[109]黄海,李长青,温志刚,彭端,尹长川,乐光新.一种零前缀OFDM系统的符号同步和载频估计算法[J].电路与系统学报,2009, 14(3):82-86.
[110] N. Han, G. Zheng, S. H. Sohn, J. M. Kim.Cyclic autocorrelation based blind OFDM detection and identification for cognitive radio[A]. IEEE Proc[C],2008:1-5.
[111] O. A. Dobre, A. Punchihewa, S. Raj, R. Inkol.Cyclostationarity-based algorithm for blind recognition of OFDM and single carrier linear digital modulations[A]. PIMRC'07[C],2007:1-5.
[112] B. R. Saltzberg.Performance of an efficient parallel data transmission system[J]. IEEE Trans. Commun. Tech.,1967, 15(6):805-811.
[113]胡苏,武刚,肖悦,李少谦.OFDM/OQAM系统中联合迭代信道估计和信号检测[J].电子与信息学报,2009, 31(10):2332-2337.
[114] B. Hirosaki.An orthogonally multiplexed QAM system using the discrete Fouriertransform[J]. IEEE Transactions on Communications,1981, 29(7):982-989.
[115] L. Vangelista, N. Laurenti.Efficient implementations and alternative architectures for OFDM-OQAM systems[J]. IEEE Transactions on Communications,2001, 49(4):
[116]张贤达.现代信号处理[M].北京:清华大学出版社, 2002.
[117]戴幻尧,蒋鸿宇,李群.基于循环谱分析的LPI信号特征检测新方法研究[J].信号处理,2009, 25(5):781-786.
[118]黄春琳.基于循环平稳特性的低截获概率信号的截获技术研究[D].长沙:国防科技大学博士学位论文, 2001.
[119] H. Zhang, A. Abdi.Cyclostationarity-based doppler spread estimation in mobile fading channels[J]. IEEE Transactions on Communications,2009, 57(4):1061-1067.
[120]徐海源.雷达/通信侦察中相位编码信号分析处理技术研究[D].长沙:国防科技大学博士学位论文, 2007.
[121]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[J].应用科学学报,2009, 27(2):138-143.
[122] D. Vucic, M. Obradovic.Cyclic spectral analysis of phase-incoherent FSK signal by matrix-based stochastic method[A]. TELSIKS 2001[C],2001:267-270.
[123] D. Vucic.Cyclic spectral analysis of frequency hopping signals[A]. 16th International Symposium on Personal, Indoor and Mobile Radio Communications[C],2005:1209-1213.
[124] D. Vucic, M. Eric, I. Pokrajac.Cyclic spectral analysis of TH-PPM UWB impulse radio signals[A]. TELSIKS 2009[C],2009:202-205.
[125] D. Vucic, M. Eric.Cyclic Spectral Analysis of UWB-IR Signals[J]. IEEE Signal Processing Letters,2009, 16(8):723-726.
[126] D. Vucic, M. Obradovic, D. Obradovic.Spectral correlation of OFDM signals related to their PLC applications[A]. 6th International Symposium on Power-Line Communications and Its Applications[C],2002:1-4.
[127]黄奇珊,彭启琮,路友荣,韩猛.OFDM信号循环谱结构分析[J].电子与信息学报,2008, 30(1):134-138.
[128] H. Zhang, D. L. Ruyet, M. Terr′.Spectral Correlation of Multicarrier Modulated Signals and Its Application for Signal Detection[J]. EURASIP Journal on Advances in Signal Processing,2010, 2010(1):1-14.
[129] H. Bolcskei.Blind estimation of symbol timing and carrier frequency offset in wireless OFDM systems[J]. IEEE Transactions on Communications 2002, 49(6):988-999.
[130] P. D. Sutton, K. E. Nolan, L. E. Doyle.Cyclostationary signatures in practical cognitive radio applications[J]. IEEE Journal on Selected Areas in Communications,2008, 26(1):13-24.
[131] W. A. Gardner, N. Antonio, L. Paura.One-bit spectral-corrlation algorithms[J]. IEEE Transactions on Signal Processing,1993, 41(1):423-427.
[132]高玉龙.任意分辨率的SSCA算法研究[J].哈尔滨工业大学学报,2008, 40(7):1040-1043.
[133] A. V. Dandawate, G. B. Giannakis.Nonparametric polyspectral estimators for kth-order(almost) cyclostationary processes[J]. IEEE Transactions on Information Theory,1994, 40(1):67-84.
[134]史建锋,朱良学.有限数据下循环谱的频域平滑对称式周期图法估计性能分析[J].数据采集与处理,2004, 19(2):155-159.
[135]郑文秀.数字通信信号的参数估计与干扰技术研究[D].西安:西安电子科技大学博士论文, 2008.
[136]周家喜,许小东,徐佩霞,戴旭初.一种CPM信号成形脉冲盲估计算法[J].电子与信息学报2009, 31(11):2639-2642.
[137]郑文秀,赵国庆,罗明.基于高阶循环累积量的OFDM子载波盲估计[J].电子与信息学报,2008, 30(02):346-349.
[138] M. Shi, Y. Bar-Ness, W. Su.Blind OFDM systems parameters estimation for software defined radio[A]. NFDSAN 07[C],2007:119-122.
[139] A. Bouzegzi, P. Ciblat, P. Jallon.New algorithms for blind recognition of OFDM based systems[J]. Signal Processing,2010, 90(3):900-913.
[140] T. van Waterschoot, V. Le Nir, J. Duplicy, M. Moonen.Analytical expressions for the power spectral density of CP-OFDM and ZP-OFDM signals[J]. IEEE Signal Processing Letters,2010, 17(4):371-374.
[141] V. Le Nir, T. Van Waterschoot, M. Moonen, J. Duplicy.Blind CP-OFDM and ZP-OFDM parameter estimation in frequency selective channels[J]. EURASIP Journal on Wireless Communications and Networking,2009, 2009(1):1-10.
[142] H. Ishii, G. W. Wornell.OFDM blind parameter identification in cognitive radios[A]. IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communication[C],2005:700-705.
[143] S. Ma, X. Pan, G. Yang, T. Ng.Blind Symbol Synchronization Based on Cyclic Prefix for OFDM Systems[J]. IEEE Transactions on Vehicular Technology,2009, 58(4):1746-1751.
[144] J. Wang, T. Chen, B. Huang.Cyclo-period estimation for discrete-time cyclo-stationary signals[J]. IEEE Transactions on Signal Processing,2006, 54(1):83-94.
[145] H. L. Hurd, N. L. Gerr.Graphical methods for determining the presence of periodic correlation[J]. J. Times Ser. Anal.,1991, 12(4):337-350.
[146]D. E. K. Martin, B. Kedem.Estimation of the period of periodically correlated sequences[J]. J. Times Ser. Anal.,1993, 14(2):193-205.
[147] A. V. Dandawate, G. B. Giannakis.Statistical tests for presence of cyclostationarity[J]. IEEE Transactions on Signal Processing,2002, 42(9):2355-2369.
[148] H. Li, Y. Bar-Ness, A. Abdi, W. Su.OFDM modulation classification and parameters extraction[A]. IEEE CROWNCOM[C],2006:1-6.
[149] F. Liedtke, U. Albers.Evaluation of features for the automatic recognition of OFDM signals in monitoring or cognitive receivers[J]. Journal of telecommunications and information technology,2008, 2008(2):30-36.
[150]赵知劲,周云水,尚俊娜.基于时频分析和RBF神经网络的调制识别[J].压电与声光,2004, 26(4):328-330.
[151] C. Nello, S. T. John.支持向量机导论[M].北京:电子工业出版社, 2005.
[152] R. Proesch.Technical handbook for radio monitoring HF[M]. Norderstedt: Books on Demand Gmbh, 2009.
[153]周家喜.非协作通信中连续相位调制信号盲解调关键技术研究[D].合肥:中国科技大学博士学位论文, 2009.
[154] G. Cariolaro.A System-Theory Approach to Decompose CPM Signals into PAM Waveforms[J]. IEEE Transactions on communications,2010, 58(1):200-210.
[155]周家喜,许小东,徐佩霞,戴旭初.一种CPM信号频率成形脉冲盲估计算法[J].电子与信息学报,2009, 31(11):2637-2642.
[156]陆明泉.同信道多信号的调制识别方法[J].清华大学学报,2009, 49(10):103-107.
[157]程汉文.基于累计量的干扰信号调制识别算法[J].电子与信息学报,2009, 31(7):1741-1745.
[158] H. T. Fu.Modulation Classification Based on Cyclic Spectral Features for Co-Channel Time-Frequency Overlapped Two-Signal[A]. PACCS[C],2009:31-34.
[159]于志明,郭黎利,孙志国.多径信道下多载波调制信号盲识别算法[J].电子与信息学报,2010, 32(7):1756-1759.