Improved Mean-Square Error Estimate for the LMS Transversal Equalizer With Narrowband Interference

• Published in: IEEE transactions on signal processing Vol. 56; no. 10; pp. 5273 - 5277
• Main Authors: Ikuma, T., Beex, A.A.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.10.2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Adaptive equalization; Adaptive equalizers; Additive noise; Algorithm design and analysis; Applied sciences; Approximation algorithms; Detection, estimation, filtering, equalization, prediction; Equalizers; Equivalence; Errors; Estimates; Exact sciences and technology; Information, signal and communications theory; Interference; Intersymbol interference; least mean-squares algorithm; Least squares approximation; Mathematical models; Miscellaneous; Narrowband; Numerical simulation; Signal and communications theory; Signal processing; Signal, noise; Steady-state; steady-state analysis; Telecommunications and information theory; Wiener filter; least mean squares algorithm; steady-state analysis; adaptive equalization; Performance evaluation; Narrow band noise; Adaptive equalizers; Adaptive equalization; Algorithm; Steady state; Mean square error; Wiener filter; least mean-squares algorithm; Signal processing; Numerical simulation; Non linear effect; Transfer function; Least mean squares methods
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2008.928502

When the least-mean-square (LMS) algorithm is used to adapt an adaptive transversal equalizer that is subject to strong narrowband interference, a so-called non-Wiener or nonlinear effect takes place. This results in the mean-square error (MSE) performance of the adaptive equalizer being better than that of the fixed Wiener filter of equivalent structure. Reuter and Zeidler proposed a transfer-function-based approach to provide an estimate of the MSE performance of the equalizer in such an environment. We have recently shown that the mean of the LMS weights in this adaptive equalizer problem shifts away from the Wiener filter solution. As a result, we propose an MSE model for the LMS equalizer that is an improvement over the existing Reuter-Zeidler model. The new model uses the same transfer-function-based approach but incorporates the shift in the mean of the weights. Numerical simulations are provided to illustrate the improvement.