An application of the expectation-maximization algorithm to interference rejection for direct-sequence spread-spectrum signals

Quan G. Zhang; Costas N. Georghiades

Kybernetika (1999)

  • Volume: 35, Issue: 1, page [83]-91
  • ISSN: 0023-5954

Abstract

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For a direct-sequence spread-spectrum (DS-SS) system we pose and solve the problem of maximum-likelihood (ML) sequence estimation in the presence of narrowband interference, using the expectation-maximization (EM) algorithm. It is seen that the iterative EM algorithm obtains at each iteration an estimate of the interference which is then subtracted from the data before a new sequence estimate is produced. Both uncoded and trellis coded systems are studied, and the EM-based algorithm is seen to perform well, outperforming a receiver that uses an optimized notch filter to remove the intereference, especially for large interference levels.

How to cite

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Zhang, Quan G., and Georghiades, Costas N.. "An application of the expectation-maximization algorithm to interference rejection for direct-sequence spread-spectrum signals." Kybernetika 35.1 (1999): [83]-91. <http://eudml.org/doc/33411>.

@article{Zhang1999,
abstract = {For a direct-sequence spread-spectrum (DS-SS) system we pose and solve the problem of maximum-likelihood (ML) sequence estimation in the presence of narrowband interference, using the expectation-maximization (EM) algorithm. It is seen that the iterative EM algorithm obtains at each iteration an estimate of the interference which is then subtracted from the data before a new sequence estimate is produced. Both uncoded and trellis coded systems are studied, and the EM-based algorithm is seen to perform well, outperforming a receiver that uses an optimized notch filter to remove the intereference, especially for large interference levels.},
author = {Zhang, Quan G., Georghiades, Costas N.},
journal = {Kybernetika},
keywords = {maximum likelihood (ML) estimation; spread-spectrum signal; sequence estimation; narrowband interference; expectation-maximization (EM) algorithm; notch filter; maximum likelihood (ML) estimation; spread-spectrum signal; sequence estimation; narrowband interference; expectation-maximization (EM) algorithm; notch filter},
language = {eng},
number = {1},
pages = {[83]-91},
publisher = {Institute of Information Theory and Automation AS CR},
title = {An application of the expectation-maximization algorithm to interference rejection for direct-sequence spread-spectrum signals},
url = {http://eudml.org/doc/33411},
volume = {35},
year = {1999},
}

TY - JOUR
AU - Zhang, Quan G.
AU - Georghiades, Costas N.
TI - An application of the expectation-maximization algorithm to interference rejection for direct-sequence spread-spectrum signals
JO - Kybernetika
PY - 1999
PB - Institute of Information Theory and Automation AS CR
VL - 35
IS - 1
SP - [83]
EP - 91
AB - For a direct-sequence spread-spectrum (DS-SS) system we pose and solve the problem of maximum-likelihood (ML) sequence estimation in the presence of narrowband interference, using the expectation-maximization (EM) algorithm. It is seen that the iterative EM algorithm obtains at each iteration an estimate of the interference which is then subtracted from the data before a new sequence estimate is produced. Both uncoded and trellis coded systems are studied, and the EM-based algorithm is seen to perform well, outperforming a receiver that uses an optimized notch filter to remove the intereference, especially for large interference levels.
LA - eng
KW - maximum likelihood (ML) estimation; spread-spectrum signal; sequence estimation; narrowband interference; expectation-maximization (EM) algorithm; notch filter; maximum likelihood (ML) estimation; spread-spectrum signal; sequence estimation; narrowband interference; expectation-maximization (EM) algorithm; notch filter
UR - http://eudml.org/doc/33411
ER -

References

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  6. Han J. C., Georghiades C. N., Maximum–likelihood sequence estimation for fading channels via the EM algorithm, In: Proc. Communication Theory Mini Conference, Houston 1993 
  7. Kaleh G. K., Joint decoding and phase estimation via the expectation–maximization algorithm, In: Proc. Internat. Symposium on Information Theory, San Diego 1990 
  8. Milstein L. B., Iltis R. A., 10.1109/MASSP.1986.1165359, IEEE ASSP Magazine (1986), 18–31 (1986) DOI10.1109/MASSP.1986.1165359
  9. Modestino J. W., Reduced–complexity iterative maximum–likelihood sequence estimation on channels with memory, In: Proc. Internat. Symposium on Information Theory, San Antonio 1993 
  10. Wu C. F., 10.1214/aos/1176346060, Ann. Statist. 11 (1983), 1, 95–103 (1983) Zbl0517.62035MR0684867DOI10.1214/aos/1176346060

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