Advanced Search
Volume 30 Issue 7
Jan.  2011
Turn off MathJax
Article Contents
Zheng Jian-ping, Bai Bao-ming, Wang Xin-mei. Digital Modulation Classification via Sequential Monte Carlo for Frequency-Nonselective Fading MIMO Channels[J]. Journal of Electronics & Information Technology, 2008, 30(7): 1571-1575. doi: 10.3724/SP.J.1146.2007.00914
Citation: Zheng Jian-ping, Bai Bao-ming, Wang Xin-mei. Digital Modulation Classification via Sequential Monte Carlo for Frequency-Nonselective Fading MIMO Channels[J]. Journal of Electronics & Information Technology, 2008, 30(7): 1571-1575. doi: 10.3724/SP.J.1146.2007.00914

Digital Modulation Classification via Sequential Monte Carlo for Frequency-Nonselective Fading MIMO Channels

doi: 10.3724/SP.J.1146.2007.00914
  • Received Date: 2007-06-11
  • Rev Recd Date: 2007-10-08
  • Publish Date: 2008-07-19
  • A modulation classification method for phase-amplitude-modulated signals transmitted through frequency-nonselective fading Multi-Input Multi-Output (MIMO) channels is presented based on Sequential Monte Carlo (SMC) method. An equivalent dynamic state space model is first derived from the MIMO channel. Then the probabilities of all possible modulation types of different transmit antennas can be calculated by sequential importance sampling and type-move step. Finally, noise average is realized utilizing the un-correlation of transmitted data symbols over N channel observation lengths. Moreover, modulations classification is achieved along with estimation of transmitted data symbols. The complexity of the proposed method is linear with the observation channel lengths, number of transmit antennas, sample size, and cardinality of modulation constellation. Simulations show that the proposed method performs well on various constellations.
  • loading
  • Long C, Chugg K, and Polydoros A. Further results inmaximum likelihood classification of QAM signals. Proc.IEEE Military Communications Conf. (MILCOM), Longbranch, NJ, 1994: 57-61.[2]Wei W and Mendel J M. A new maximum-likelihood methodfor modulation classification. Proc. Asilomar Conf., PacificGrove, CA, 1996: 1132-1135.[3]Soliman S and Hsue S Z. Signal classification using statisticalmoments[J].IEEE Trans. on Signal Processing.1996, 44(11):2793-2800[4]Assaleh K, Farrell K, and Mammone R J. A new method ofmodulation classification for digitally modulated signals.Proc. MILCOM, San Diego, CA, 1992: 712-716.[5]Swami A and Sadler B M. Hierarchical digital modulationclassification using cumulants[J].IEEE Trans. on Commun.2000, 48(3):416-429[6]Lesage S, Tourneret J Y, and Djuric P M. Classification ofdigital modulations by MCMC sampling. Proc. Int. Conf.Acoustics, Signal Processing, Communications, and Control.Englewood Cliffs, NJ: Prentice-Hall, 1995: 2553-2556.[7]Drumright T and Ding Z. QAM constellation classificationbased on statistical sampling for linear distortive channels[J].IEEE Trans. Signal Processing.2006, 54(5):1575-1586[8]Doucet A, de Freitas J F G, and Gordon N. Sequential MonteCarlo Methods in Practice. New York, Springer-Verlag, 2001.[9]Huang Y, Zheng J, and Djuric P M. Bayesian detection forBLAST[J].IEEE Trans. on Signal Processing.2005, 53(3):1086-1096[10]Dong B, Wang X, and Doucet A. A new class of soft MIMOdemodulation algorithms[J].IEEE Trans. on Signal Processing.2003, 51(11):2752-2763[11]Proakis J G. Digital Communications. 4th ed. New York:McGraw-Hill, 2001, Chapter 10.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (3154) PDF downloads(739) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return