Initialization Techniques for Improved Convergence of LMS DFEs in Strong Interference Environments

— 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 the Wiener 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...

• Type: Conference
• Year: 2007
• Where: GLOBECOM
• Authors: Arun Batra, James R. Zeidler, Aloysius A. Beex
• Keywords: Convergence Time, GLOBECOM 2007, Telecommunications , Theoretical Wiener filter, Wiener filter