Suppression Of Narrowband Interference Via Single Channel Adaptive Preprocessing

Satorius, E.; Krishnan, Suraj; Yu, Xindi; Griffiths, L.J.; Reed, I. S.; Truong, Trieu‐Kien

doi:10.1109/acssc.1988.753997

Cited by 5 publications

(1 citation statement)

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“…A two-stage system employing a linear predictor has been previously investigated [21,22] in combination with the constant modulus algorithm (CMA). The prediction filter is employed to mitigate the interference and ensure that the CMA locks on to the signal of interest.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Approach for Improving the Convergence of Least-Mean-Square Adaptive Decision-Feedback Equalizers in the Presence of Severe Narrowband Interference

Batra

Zeidler

Beex

2007

EURASIP J. Adv. Signal Process.

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It has previously been shown that a least-mean-square (LMS) decision-feedback filter can mitigate the effect of narrowband interference (L.-M. Li and L. Milstein, 1983). An adaptive implementation of the filter was shown to converge relatively quickly for mild interference. It is shown here, however, that in the case of severe narrowband interference, the LMS decision-feedback equalizer (DFE) requires a very large number of training symbols for convergence, making it unsuitable for some types of communication systems. This paper investigates the introduction of an LMS prediction-error filter (PEF) as a prefilter to the equalizer and demonstrates that it reduces the convergence time of the two-stage system by as much as two orders of magnitude. It is also shown that the steady-state bit-error rate (BER) performance of the proposed system is still approximately equal to that attained in steady-state by the LMS DFE-only. Finally, it is shown that the two-stage system can be implemented without the use of training symbols. This two-stage structure lowers the complexity of the overall system by reducing the number of filter taps that need to be adapted, while incurring a slight loss in the steady-state BER.

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Section: Introductionmentioning

confidence: 99%