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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 電信工程學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/63099
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor陳光禎(Kwang-Cheng Chen)
dc.contributor.authorTsung-Min Linen
dc.contributor.author林聰岷zh_TW
dc.date.accessioned2021-06-16T16:22:39Z-
dc.date.available2013-02-01
dc.date.copyright2013-02-01
dc.date.issued2013
dc.date.submitted2013-01-28
dc.identifier.citation[1] John. G. Proakis, Digital communication, 4th ed. McGraw-Hill, 2001.
[2] Harry. L. Van. Trees, Detection, estimation and modulation theory, part I: detection, estimation, and linear modulation theory, Wiley Inter-science, 2001.
[3] Theodore S. Rappaport, Wireless communications, 2nd Prentice-Hall PTR, 2002.
[4] Peyton Z. Peebles, Jr., Probability random variables and random signal principles, 4th ed. McGraw-Hill, 2001.
[5] Chrysostomos L. Nikias and Athina P. Petropulu, Higher-order spectra analysis, Prentice-Hall, 1993.
[6] Asoke Kumar Nandi, Blind estimation using higher-order statistics, Kluwer Academic Publishers, 1999.
[7] Jerry M. Mendel, “Tutorial on higher-order statistics (spectra) in signal process and system theory: theoretical result and some applications,” Proceedings of the IEEE, vol. 79, no. 3, pp.278-305, March 1991.
[8] Jefferson L.Xu, Wei Su, and Mengchu Zhou, “Likelihood-ratio approaches to automatic modulation classification,” IEEE Trans. on Systems, Man, and Cybernetics-Part C: Applications and Reviews, vol.41, no. 4, pp. 455-469, 2011.
[9] Fahed Hameed, Octavia A. Dobre, and Dimitrie C. Popescu, “On the likelihood-based approach to modulation classification,” IEEE Trans. on Wireless Communications, vol. 8, no. 12, pp.5884-5892, Dec. 2009.
[10] W. Wen and J. M. Mendel, “Maximum-likelihood classification for digital amplitude-phase modulations,” IEEE Trans. Communications, vol. 48, no. 2, pp. 189–193, Feb. 2000.
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[12] Prokopios Panagiotou, Achilleas Anastasopoulos and Andreas Polydoros, “Likelihood ratio tests for modulation classification,” IEEE MILCOM, vol. 2, pp. 670–674, Oct. 2000.
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[15] Samir S. Soliman and Shue-Zen Hsue, “Signal classification using statistical moments”, IEEE Trans. Communications, vol. 40, no. 5, pp. 908-916, May. 1992.
[16] Liang Hong and K. C. Ho, “Identification of digital modulation types using the wavelet transform,” Military Communications Conference Proceedings, vol. 1, pp. 427-431, Oct. 31- Nov. 3, 1999.
[17] K.C.Ho, W. Prokopiw, and Y.T.Chan, “Modulation identification of digital signals by the wavelet transform,” IEE Radar, Sonar and Navigation, vol. 147, pp. 169-176, 2000.
[18] Wu Dan, Gu Xuemai, Guo Qing, “A new scheme of automatic modulation classification using wavelet and WSVM,” International Conference on Mobile Technology Applications and Systems, pp. 1-5, Nov. 2005.
[19] Barathram Ramkumar, “Automatic modulation classification for cognitive radios using cyclic feature detection,” IEEE Circuits and Systems Magazine, pp. 27-45, second quarter 2009.
[20] A. Swami and B. M. Sadler, “Hierarchical digital modulation classification using cumulants,” IEEE Trans. on Wireless Communications, vol. 48, no. 3, pp. 416–429, Mar. 2000.
[21] Lei Shen, Shiju Li, Chen Seng Song and Fangni Chen, “Automatic Modulation classification of MPSK signals using high order cumulants,” Eighth International Conference on Signal Processing, 2006.
[22] Xin Zhou, Ying Wu and Bin Wang, “The MPSK signals modulation classification based on Kernel methods,” Eighth International Symposium on Antennas, Propagation and EM Theory, pp.1419-1422, 2008.
[23] Chisheng Li, Jing Xiao and Qingyi Xu, “A Novel modulation classification for PSK and QAM signals in wireless communication,” IET International Conference on Communication Technology and Application, pp. 89-92, 2011.
[24] Lin Yang, Zhi Ji, Xiaoding Xu, Xuchu Dai and Peixia Xu, “Modulation classification in multipath fading environments,” Fourth International Symposium on Wireless Communication Systems, pp.171-174, 2007.
[25] Gangcan Sun, “MPSK signals modulation classification using sixth-order cumulants,” Third International Congress on Image and Signal Processing, pp. 4404-4407, 2010.
[26] M.R. Mirarab and M.A. Sobhani, “Robust mdulation classification for PSK/QAM/ASK using higher-order cumulants,” Sixth International Conference on Information, Communications & Signal Processing, pp.1-4, 2007.
[27] Juha Venalainen, Liisa Terho and Visa Koivunen, “Modulation classification in fading multipath channel,” Thirty-sixth Asilomar Conference on Signals, Systems and Computers, vol. 2, pp. 1890–1894, 2002.
[28] Hsiao-Chun Wu, Mohammad Saquib and Zhifeng Yun, “Novel automatic modulation classification using cumulants feature for communication via multipath channel,” IEEE Trans. on Wireless Communications, vol. 7, no. 8, pp. 3098–3105, 2008.
[29] Miao Shi, Amir Laufer, Yeheskel Bar-Ness and Wei Su, “Fourth order cumulants in distinguishing single carrier from OFDM signals,” IEEE MILCOM, pp. 1-6, 2008.
[30] Said E.El-khamy, Hend A. Elsayed and Mohamed M. Rizk, “Classification of multi-user chirp modulation signals using higher-order cumulant features and four types of classifiers,” Twenty-eighth National Radio Science Conference, pp. 1-10, 2011.
[31] Ataollah Ebrahimzadeh, Maryam Ebrahimzadeh, “An expert system for digital signal type classification,” Journal of Electrical Engineering, vol. 58, no. 6, pp. 334-341, 2007.
[32] Nader Sheikholeslami Alagha, “Cramer-Rao bounds of SNR estimates for BPSK and QPSK modulated signals,” IEEE Communication Letters, vol. 5, no. 1, pp. 10-12, Jan. 2001.
[33] Steven M. Kay, Fundamentals of statistical signal process vol.1-estimation theory, Prentice Hall, 1993.
[34] Ronald L. Allen and Duncan W. Mills, Signal Analysis, Wiley-Interscience, 2004.
[35] Athanasios Papoulis and S. Unnikrishna Pillai, Probability, random variables and stochastic processes, 4th ed. McGraw-Hill, 2002.
[36] O. A. Dobre, A. Abdi, Y. Bar-Ness and W. Su, “Survey of automatic modulation classification techniques: classical approaches and new trends,” IET Communications, pp. 137-156, 2007.
[37] Liang Hong, “Classification of BPSK and QPSK signals in fading environment using the ICA technique,” Thirty-seventh Southeastern Symposium on System Theory, pp. 491-494, 2005.
[38] A. Abdi, O. A. Dobre, R. Choudhry, Y. Bar-Ness, and W. Su, “Modulation classification in fading channel using antenna arrays,” IEEE MILCOM, vol. 1, pp. 211-217, 2004.
[39] Chad. M. Spooner, “On the utility of sixth-order cyclic cumulants for RF signal classification,” Signals, Systems and Computers 2001, Conference Record of the Thirty-fifth Asilomar Conference, vol. 1, pp. 890-897, 2001.
[40] Kai-Zhi Chen and Ai-Qun Hu, “MPSK demodulation algorithm based on pattern recognition,” International Conference on Neural Networks & Signal Processing, pp. 182-186, 2008.
[41] Hai-Bing Guan, Chen-Zhou Ye, and Xiao-Yong Li, “Modulation classification based on spectrogram,” Third International Conference on Machine Learning and Cybernetics, pp. 3551-3556, 2004.

[42] O. A. Dobre, Y. Bar-Ness, and W. Su, “Higher-order cyclic cumulants for high order modulation classification,” IEEE MILCOM, vol. 1, pp.112-117, 2003.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/63099-
dc.description.abstract綜觀軍事通訊發展,諸如頻譜管理、軍事監控、訊號情報蒐集、電子戰運用,大多通聯訊號相關參數無法事前獲得,因此如何在數據訊號偵收系統截獲敵方之未知通聯訊號時,能夠即時完成訊號辨識、解調,快速獲得重要情報,變成一個重要課題,其中訊號類型自動辨識及分類技術,成為接收端於射頻訊號截收至基頻訊號解調過程之必要執行項目,通聯訊號自動分類技術依基底區分2類,分別為假設判別(decision-theoretic)基底及特徵識別(pattern-recognition)基底,在本篇論文中,主要採用屬於特徵識別為基底之高階統計法則(moments以及cumulants),實現常用於軍事通聯之相位鍵移調變(M-ary PSK)類型自動分類技術,因此本論文首先分析並模擬實現2階、4階、6階及8階等高階統計法則在所遭遇條件下(如理想情形、頻率偏移、相位偏移及時間偏移)展現特性及影響,接著就些許現已發表相關論文,模擬在多重路徑傳輸通道及可加性高斯白雜訊(AWGN)條件下之效能分析及遭遇困境,最後引出本論文針對困境所提出相位鍵移調變訊號分類法則,敘述如下:
1.使用4階moments進行通道脈衝響應參數估測及通道補償作業。
2.採用階級式(hierarchical)架構進行訊號分類,分別使用經過通道補償後之2階cumulants之特徵純量(feature scale)及4階cumulants之特徵純量。
數值分析結果顯示,當相位鍵移調變訊號於多重路徑傳輸條件下,本篇論文所提法則較其他採用4階cumulants之特徵向量(feature vector)或6階cumulants之特徵向量具備較佳分類效能,可有效運用於相位鍵移調變訊號自動分類應用上。
zh_TW
dc.description.abstractWhile investigating the development of military communications such as spectrum management, signal intelligence (SIGINT) collection and electronic warfare, most signal-related parameters are unavailable in advance. It has become an important topic to recognize the unknown signals and then extract the SIGINT from the signals quickly when these signals emitted from the enemy are intercepted by any signals-collection system. Automatic modulation recognition and classification has become a necessary step between signal detection and signal demodulation. Automatic modulation classification could be divided into two subgroups, decision-theoretic based and pattern-recognition based. In the thesis, higher-order statistics, moments and cumulants, is adopted to classify M-ary PSK (M-PSK) signals widely used by military communications. Firstly, second-order cumulants to eight-order cumulants are implemented and analyzed under the scenarios of the ideal situation, frequency offset, phase rotation, phase noise, timing offset and AWGN. Secondly, multipath fading channel is unavoidable in practice. So that performance comparison and drawback of some published articles are also implemented and discussed in detail while the transmitted signal is corrupted by multipath fading and AWGN. Finally, the proposed method in the thesis to recognize M-PSK signals is presented as following statements:
(1)Blind channel estimation and compensation are carried out via fourth-order moments of the received signal without any a priori information.
(2)The structure of proposed modulation classification method is hierarchical. Normalized second-order cumulants and normalized fourth-order cumulants after blind channel compensation are adopted to classify M-PSK signals.
After the simulation and numerical analysis, the proposed method could get better performance than some other methods which adopt the feature vector of fourth-order cumulants or sixth-order cumulants as the feature extraction in multipath fading channel. The proposed method could be used in M-PSK signals classification efficiently.
en
dc.description.provenanceMade available in DSpace on 2021-06-16T16:22:39Z (GMT). No. of bitstreams: 1
ntu-102-P94942010-1.pdf: 2797291 bytes, checksum: 02593a974d74f0570f597021acbe9d09 (MD5)
Previous issue date: 2013
en
dc.description.tableofcontents口試委員審定書 I
誌謝 II
摘要 III
Abstract IV
List of Figures VIII
Chapter 1 Introduction 1
1.1 Military Communications 1
1.2 Thesis Organization 3
Chapter 2 Automatic Modulation Classification 5
2.1 AMC Related Technology Overview 5
2.1.1 Decision-Theoretic Methods 5
2.1.2 Pattern Recognition Methods 6
2.2 Higher-Order Statistics (HOS) 7
2.2.1 Moments and Cumulants 7
2.2.2 Nth-Order Cumulants for M-PSK Signals 10
Chapter 3 Practical Factors and Existing Classifiers 13
3.1 Practical Factors Influencing HOS 13
3.1.1 Carrier Phase Offset 13
3.1.2 Phase Noise 15
3.1.3 Carrier Frequency Offset 17
3.1.4 Timing Offset 18
3.1.5 Pulse Shaping 20
3.1.6 AWGN 22
3.1.7 Multipath Fading Channel 24
3.2 Some Existing Nth-Order Cumulants Classifiers 27
3.2.1 One-Shot Structure Classifiers 28
3.2.1.1 Using Feature Vector of Fourth-Order Cumulants 28
3.2.1.2 Using Feature Vector of Sixth-Order Cumulants 32
3.2.1.3 Using Feature Scale of Fourth-Order Cumulants 37
3.2.1.4 Using Feature Scale of Eighth-Order Cumulants 42
3.2.2 Hierarchical Structure Classifiers 46
3.2.2.1 Using Fourth-Order Cumulants 46
3.2.2.2 Using Second-Order and Fourth-Order Cumulants 51
3.2.3 Performance Comparison of Existing and Possible Classifiers 55
3.2.3.1 Performance Comparison in AWGN 55
3.2.3.2 Performance Comparison in Fading Channel 58
Chapter 4 Proposed Classifier and Numerical Results 62
4.1 Proposed Classifier 62
4.1.1 Blind Channel Estimation and Compensation 63
4.1.2 Pattern-Recognition Based and Hierarchical 64
4.2 Numerical Results 67
4.2.1 The Improvement after Blind Channel Estimation 68
4.2.2 Comparison with Other Classifiers 75
Chapter 5 Conclusion and Future Works 85
5.1 Conclusion 85
5.2 Future Works 86
Appendix 2.A 88
Appendix 4.A 91
Bibliography 132
dc.language.isoen
dc.title使用高階統計法則實現相位鍵移調變訊號分類作業zh_TW
dc.titleM-ary PSK Signals Classification Using Higher-Order Statisticsen
dc.typeThesis
dc.date.schoolyear101-1
dc.description.degree碩士
dc.contributor.oralexamcommittee蘇炫榮(Hsuan-Jung Su),葉丙成(Ping-Cheng Yeh),白宏達(Hung-Ta Pai),李佳翰(Chia-han Lee)
dc.subject.keyword自動調變分類,高階統計,相位鍵移調變訊號分類,訊號辨識技術,多重路徑通道估測,階級式訊號分類技術,zh_TW
dc.subject.keywordAutomatic Modulation Classification,HOS (Higher-Order Statistics),M-ary PSK signals classification,signal recognition,multipath channel estimation,hierarchical based modulation classification,en
dc.relation.page137
dc.rights.note有償授權
dc.date.accepted2013-01-29
dc.contributor.author-college電機資訊學院zh_TW
dc.contributor.author-dept電信工程學研究所zh_TW
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