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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 [1]. 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 pre-filter 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.

• Adaptive Equalization
• Interference Mitigation
• Linear Prediction
• Convergence