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Keywords:
maximum likelihood (ML) estimation; spread-spectrum signal; sequence estimation; narrowband interference; expectation-maximization (EM) algorithm; notch filter
Summary:
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.
References:
[1] Ansari A., Viswanathan R.: Application of expectation–maximization algorithm to the detection of a direct–sequence signal in pulsed noise jamming. IEEE Trans. Comm. 41 (1993), 1151–1154 DOI 10.1109/26.231956 | Zbl 0800.94120
[2] Dempster A. P., Laird N. M., Rubin D. B.: Maximum–likelihood from incomplete data via EM algorithm. J. Roy. Statist. Soc. 39 (1977), 1–17 MR 0501537
[3] Georghiades C. N., Han J. C.: Optimum decoding of TCM in the presence of phase–errors. In: Proc. 1990 International Symposium and Its Applications (ISITA’90), Hawaii 1990
[4] Georghiades C. N., Han J. C.: Sequence estimation in the presence of random parameters via the EM algorithm, submitte.
[5] Georghiades C. N., Snyder D. L.: The expectation–maximization algorithm for symbol unsynchronized sequence detection. In: IEEE Trans. Comm. COM-39 (1991), 54–61 DOI 10.1109/26.68276
[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.: Signal processing for interference rejection in spread spectrum communications. IEEE ASSP Magazine (1986), 18–31 DOI 10.1109/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.: On the convergence properties of the EM algorithm. Ann. Statist. 11 (1983), 1, 95–103 DOI 10.1214/aos/1176346060 | MR 0684867 | Zbl 0517.62035
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