應用高階統計之特徵法自動調變辨識技術｜國立中央大學博碩士論文系統

簡易檢索 / 詳目顯示

回結果列表

研究生：	施博淦 Po-kuan Shih
論文名稱：	應用高階統計之特徵法自動調變辨識技術 A Feature-Based Automatic Modulation Classification Technique Using High-Order Statistics
指導教授：	林嘉慶 Jia-chin Lin 張大中 Dah-chung Chang
口試委員:
學位類別：	碩士 Master
系所名稱：	資訊電機學院 - 通訊工程學系 Department of Communication Engineering
畢業學年度：	99
語文別：	英文
論文頁數：	59
中文關鍵詞：	特徵法自動調變辨識、多路徑衰減通道、自回歸通道模型、統計量、高階統計值
外文關鍵詞：	feature-based AMC (FB-AMC), multipath fading channel, autoregressive channel model, high-order statistics, cumulant
相關次數：	點閱：19 下載：0
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

在訊號辨識的領域裡，自動訊號辨識是一門古典的題目。這項技術較常應用在當傳送訊號具有可適性時的情況。針對通過非AWGN通道的訊號進行辨識的工作至今仍是一項困難的挑戰，高階統計法是最常被設計應用於針對此狀況的辨識技術。我們使用高階統計參數來估測通道係數，並使用累計量來設計一個多階層決策架構。我們將討論在靜態和時變通道模型下的演算法差異，並比較在不同接收條件下的辨識率。

Automatic modulation classification (AMC) is a classical topic in signal classification field. This technique is often used when the transmitted signals are adaptive. So far, recognition of signals passing through non-AWGN channels is still a hard task. High-order statistics is the most adopted method of being designed for classification in non-AWGN situations. We use high-order statistical parameters to obtain estimated channel coefficients and design a multiple-layered decision structure with cumulants. We will discuss the difference of algorithms for static and time-varying channel models, and compare the classification rate in different receiving conditions.

Contents .................................................................................. III
List of Figures .......................................................................... V
List of Tables ........................................................................... VI
Chapter 1 Introduction ............................................................ 1
1 Background .......................................................................................................... 1
2 Motivation ............................................................................................................ 2
3 Organization ......................................................................................................... 3
Chapter 2 Classification Algorithm ........................................ 4
1 Performance Measurement ................................................................................... 4
2 Categories of Classification Algorithms .............................................................. 5
3 High-Order Statistics ............................................................................................ 6
3.1 Basic Introduction.......................................................................................... 6
3.2 Gaussian Processes ........................................................................................ 9
4 System Description ............................................................................................ 10
5 AWGN Channels ................................................................................................ 11
6 Multipath Fading Channels ................................................................................ 13
6.1 Signal Model................................................................................................ 14
6.2 Normalized Cumulant Feature ..................................................................... 14
6.3 Received Cumulant...................................................................................... 15
6.4 Channel Preprocessing-Fixed Tap Position ................................................. 16
Chapter 3 Proposed Algorithm ............................................. 19
1 Preliminary ......................................................................................................... 19
2 HOS Features Selection ..................................................................................... 19
3 Decision Technique ............................................................................................ 21
4 Channel Preprocessing-Most Dominant Path .................................................... 23
5 Time-Varying Multipath Channels .................................................................... 26
5.1 Signal Model................................................................................................ 26
5.2 Received Cumulants .................................................................................... 27
5.3 Modified Algorithm-Partial Statistics ......................................................... 27
Chapter 4 Simulation Results ................................................ 30
1 AWGN Channels ................................................................................................ 30
2 Static Multipath Channels .................................................................................. 32
2.1 Comparison with Single Cumulant, {BPSK, QPSK} ................................. 33
2.2 Comparison with Single Cumulant, {BPSK, QPSK, 16QAM, 64QAM} ... 35
2.3 Comparison with Decision Tree .................................................................. 37
3 Time-Varying Multipath Channels .................................................................... 43
4 Performance Analysis ........................................................................................ 45
Chapter 5 Conclusions ........................................................... 46
Appendix .................................................................................. 47
A. Signal Model for Static Channels ........................................................................ 47
B. Signal Model for Time-Varying Channels .......................................................... 53
Bibliography ............................................................................ 57

                                

[1] L. Bixio, G. Oliveri, M. Ottonello, and C. S. Regazzoni, "OFDM recognition
based on cyclostationary analysis in an open spectrum scenario," in 2009 IEEE
69th Vehicular Technology Conference, 2009, pp. 1-5.
[2] J. Mitola III, "Cognitive radio for flexible mobile multimedia communications ,"
in 1999 IEEE Int. Workshop on Mobile Multimedia Communications, 1999, pp.
3-10.
[3] T. Yucek and H. Arslan, "A novel sub-optimum maximum-likelihood modulation
classification algorithm for adaptive OFDM systems ," in 2004 IEEE Wireless
Communications and Networking Conf., 2004, pp. 739-744.
[4] O.A. Dobre, A. Abdi, Y. Bar-Ness, and W. Su, "Survey of automatic modulation
classification techniques: classical approaches and new trends," IET
Communications, vol. 1, no. 2, pp. 137-156, Apr. 2007.
[5] A. Swami and B.M. Sadler, "Hierarchical digital modulation classification using
cumulants," IEEE Trans. on Communications, vol. 48, no. 3, pp. 416-429, Mar.
2000.
[6] L. Yang, Z. Ji, X. Xu, X. Dai, and P. Xu, "Modulation classification in multipath
fading environments," in 2007 4th Int. Symp. on Wireless Communication
Systems, 2007, pp. 171-174.
[7] N. An, B. Li, and M. Huang, "Modulation classification of higher order MQAM
signals using mixed-order moments and Fisher criterion," in 2010 The 2nd Int.
Conf. on Computer & Automation Engineering, 2010, pp. 150-153.
[8] W. Su, J. L. Xu, and M. Zhou, "Real-time modulation classification based on
maximum likelihood," IEEE Communications Letters, vol. 12, no. 11, pp.
801-803, Nov. 2008.
[9] S. Lesage, J.Y. Tourneret, and P.M. Djuric, "Classification of digital modulations
by MCMC sampling," in Proc. 2001 IEEE Int. Conf. on Acoustics, Speech &
Signal Processing, 2001, pp. 2553-2555.
[10] F. Hameed, O. A. Dobre, and D. C. Popescu, "On the likelihood-based approach
to modulation calssification," IEEE Trans. on Wireless Communications, vol. 8,
no. 12, pp. 5884-5892, Dec. 2009.
[11] E.E. Azzouz. and A.K. Nandi, "Algorithms for automatic modulation recognition
of communication signals," IEEE Trans. on Communications, vol. 46, pp. 431-436, Apr. 1998.
[12] F. Wang and X. Wang, "Fast and robust modulation classification via
Kolmogorov-Smirnov test," IEEE Trans. on Communications, vol. 58, no. 8, pp.
2324-2332, Aug. 2010.
[13] K. C. Ho, W. Prokopiw, and Y. T. Chan, "Modulation identification of digital
signals by the wavelet transform," IEE Proc. Radar, Sonar, & Navigation, vol.
147, no. 4, pp. 169-176, Aug. 2000.
[14] L. Hong and K.C. Ho, "Identification of digital modulation types using the
wavelet transform," in Proc. 1999 IEEE Military Communications, 1999, pp.
427-431.
[15] Henry Stark and John W. Woods, Probability and Random Processes with
Applications to Signal Processing, 3rd ed.: Prentice Hall, 2002.
[16] C. L. Nikias and A.P. Petropuou, Higher-Order Spectral Analysis.: Prentice Hall,
1993.
[17] P. Li, F. Wang, and Z. Wang, "Algorithm for modulation recognition based on
high-order cumulants and subspace decomposition," in 2006 8th Int. Conf. on
Signal Processing, 2006.
[18] W. Akmouche, "Detection of multicarrier modulations using 4th-order
cumulants," in Proc. 1999 IEEE Military Communications, 1999, pp. 432-436.
[19] A. M. Al-Smadi, "AR model identification using higher order statistics," in 2007
IEEE Int. Conf. on Computer Systems & Applications, 2007, pp. 588-591.
[20] L. Chen, H. Kusaka, M. Kominami, and Q. Yin, "Blind identification of
noncausal AR models based on higher-order statistics," IEEE Trans. on Signal
Processing, vol. 48, no. 1, pp. 27-36, Jan. 1996.
[21] H. H. Chiang and C. L. Nikias, "Adaptive deconvolution and identification of
nonminimum phase FIR systems based on cumulants," IEEE Trans. on
Automatic Control, vol. 35, no. 1, pp. 36-47, Jan. 1990.
[22] T. Mei, F. Yin, and J. Wang, "Blind source separation based on cumulants with
time and frequency non-properties," IEEE Trans. on Audio, Speech, & Language
Processing, vol. 17, no. 6, pp. 1099-1108, Aug. 2009.
[23] L. Shen, S. Li, S. Song, and F. Chen, "Automatic modulation classification of
MPSK signals using high order cumulants," in 2006 8th Int. Conf. on Signal
Processing, 2006.
[24] J. C. Lin, "Blind equalisation technique based on an improved constant modulus
adaptive algorithm," IEE Proc. Communications, vol. 149, no. 1, pp. 45-50, Feb.
2002.
[25] M.R. Mirarab and M.A. Sobhani, "Robust modulation classification for
PSK/QAM/ASK using higher-order cumulants," in 2007 6th Int. Conf. on
Information, Communications & Signal Processing, 2007, pp. 1-4.
[26] H.C. Wu, M. Saquib, and Z. Yun, "Novel automatic modulation classification
using cumulant features for communications via multipath channels," IEEE
Trans. on Wireless Communications, vol. 7, no. 8, pp. 3098-3105, Aug. 2008.
[27] V.D. Orlic and M. L. Dukic, "Multipath channel estimation algorithm for
automatic modulation classification using sixth-order cumulants," IEEE
Electronics Letters, vol. 46, no. 19, p. 1348, Sep. 2010.
[28] B. Ramkumar, T. Bose, and M. S. Radenkovic, "Robust multiuser automatic
modulation classifier for multipath fading channels," in 2010 IEEE Symp. on
New Frontiers in Dynamic Spectrum, 2010, pp. 1-10.
[29] V. Orlic and M.L. Dukic, "Algorithm for automatic modulation classification in
multipath channel based on sixth-order cumulants," in 2009 9th Int. Conf. on
Telecommunication in Modern Satellite, Cable & Broadcasting Services, 2009,
pp. 423-426.
[30] C.S. Park and D.Y. Kim, "A novel robust feature of modulation classification for
reconfigurable software radio," IEEE trans. on Consumer Electronics, vol. 52,
no. 4, pp. 1193-1200, Nov. 2006.
[31] G. Han, J. Li, and C. Chen, "A robust MDPSK modulation classifier based on
cumulants," in 2003 17th Int. Conf. on Advanced Information Networking &
Applications, 2003, pp. 265-268.
[32] Z. Shan, Z. Xin, and W. Ying, "Improved modulation classification of MPSK
signals based on high order cumulants," in 2010 2nd Int. Conf. on Future
Computer & Communication, 2010, pp. 444-448.
[33] G. Sun, "MPSK signals modulation classification using sixth-order cumulants,"
in 2010 3rd Int. Congr. on Image & Signal Processing, 2010, pp. 4404-4407.

簡易檢索 / 詳目顯示

相關論文