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Abstract—The least-mean square (LMS) decision-feedback
equalizer (DFE) was previously shown [1], [2] to possess an extended
convergence time in an interference limited environment.
In [1] it was shown that the convergence time can be significantly
reduced by using the received samples and the training data
to initialize (data-aided initialization) the LMS weights with
an estimate for the Wiener weights. In this paper, two dataaided
initialization techniques for equalization in the presence of
severe narrowband interference are discussed and compared. The
estimate of theWiener filter is obtained from data-based averages
of the autocorrelation matrix and the cross-correlation vector.
The first technique is the Multistage Wiener Filter (MSWF)
first proposed by Goldstein, et al. [3]. This algorithm provides
a reduced complexity approach by approximating the Wiener
filter in a lower dimensional subspace. The second technique
is a parametric approximation to the Direct Matrix Inversion
(DMI) solution based on the Gohberg-Semencul formula [4],
[5] to obtain the inverse in a computationally efficient fashion.
Both techniques were compared in terms of complexity (i.e. the
number of multiplications required) and BER performance as
compared to the theoretical Wiener filter for the DFE. The
MSWF requires fewer training symbols than the approximation
to the DMI solution in order to approach the BER performance of
the theoreticalWiener filter. The parametric approximation to the
DMI solution is computationally efficient but exhibits instability
due to assumptions made on the structure of the correlation
matrix and when the minimum eigenvalue is close to zero at
high SNR.
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