Interference Rejection for Spread Spectrum Signals Using the EM Algorithm
Spread-spectrum systems are well known to be robust to narrowband interference. Even for these systems, however, performance degrades substantially when interference levels rise, requiring the use of further signal processing to combat interference. Traditionally, the approach has been to excise the interference by either estimating it and then subtracting it, or by filtering it out. In this paper we instead formulate the problem as one of maximum-likelihood (ML) sequence estimation in the presence of interference, and then solve it by using the expectation-maximization (EM) algorithm. We look first at the problem of signle-tone interference, and then generalize it to the case of narrowband Gaussian interference. Comparisons are made to the optimum receiver, and to a receiver that utilize a notch-filter to reject the interference. The EM algorithm is seen to perform esentially optimally, even for large interference levels.
KeywordsSequence Estimation Processing Gain Narrowband Interference Interference Rejection Conventional Receiver
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- 1.L.B. Milstein, “Interference Rejection Techniques in Spread Spectrum Communications,” Proceedings of the IEEE, vol. 76, No. 6, June 1988.Google Scholar
- 2.J.D. Laster and J.H. Reed, “Interference Rejection in Digital Wireless Communications,” IEEE Signal Processing Magazine, pp. 37–62, May 1997.Google Scholar
- 7.H.V. Poor, “On parameter Estimation in DS/SSMA Formats,” Proceedings of Advances in Communications and Control Systems, Baton Rouge, Louisiana, pp. 59–70, October 1988.Google Scholar
- 8.G.K. Kaleh, “Joint Decoding and Phase Estimation Via the Expectation-Maximization Algorithm,” Proceedings of the International Symposium on Information Theory, San Diego, January, 1990.Google Scholar
- 12.C.N. Georghiades, “Algorithms for Joint Synchronization and Detection,” in Coded Modulation and Bandwidth-Efficient Transmission, Elsevier Science Publishers, 1992.Google Scholar
- 13.J.W. Modestino, “Reduced-complexity Iterative Maximum-Likelihood Sequence Estimation on Channels with Memory,” Proceedings of the International Symposium on Information Theory, San Antonio, Texas, January 1993.Google Scholar
- 15.C.N. Georghiades and J.C. Han, “On the Application of the EM Algorithm to Sequence Estimation for Degraded Channels,” Proceedings of the 32-nd Allerton Conference, University of Illinois, September 1994.Google Scholar
- 16.J. Fessler, A.O. Hero, “Space-Alternating Generalized Expectation- Maximization Algorithm,” IEEE Transactions on Signed Processing, vol. 42, No. 10, October 1994.Google Scholar
- 19.Q. Zhang and C.N. Georghiades, “An Application of the EM Algorithm to Sequence Estimation in the Presence of Tone Interference,” Proceedings of the 5th IEEE Mediterranean Conference on Control and Systems, Paphos, Cyprus, July 1997.Google Scholar
- 20.T.K. Moon, “The Expectation-Maximization Algorithm,” IEEE Signal Processing Magazine, pp. 47–60, November 1996.Google Scholar