Abstract
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 asmuch 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 systemby reducing the number of filter taps that need to be adapted, while incurring a slight loss in the steady-state BER.
Highlights
Maintaining reliable wireless communication performance is a challenging problem because of channel impairments such as fading, intersymbol interference (ISI), narrowband interference, and noise
To reduce the convergence time and the number of training symbols needed, we propose a two-stage system that uses an LMS prediction-error filter (PEF) as a prefilter to the LMS decision-feedback equalizer (DFE)-only
The DFE steady√-state bit-error rate (BER) results in the convergence plots are given by Q( signal-to-interference-plus-noise ratio (SINR)), where Q(·) is the Q-function [29, page 40] and the SINR is given in (15)
Summary
Maintaining reliable wireless communication performance is a challenging problem because of channel impairments such as fading, intersymbol interference (ISI), narrowband interference, and noise. In [17] it is shown that an extended RLS filter that estimates the chirp rate of the input signal can minimize the tracking errors associated with the RLS algorithm and provides performance that exceeds that of LMS. It should be noted, that the improved tracking performance requires a significant increase in computational complexity and knowledge that the underlying variations in the input signal can be accurately modeled by a linear FM chirp. To reduce the convergence time and the number of training symbols needed, we propose a two-stage system that uses an LMS prediction-error filter (PEF) as a prefilter to the LMS DFE-only.
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