Principles of a Linear Adaptive Equalizer with LMS Algorithm Implementation

This document presents the schematic diagram of a linear adaptive equalizer (refer to "Introduction to Modern Digital Signal Processing" p.275 for illustration) implemented using the LMS algorithm. Key implementations include plotting the mean-squared error convergence curve for a single trial, deriving final filter coefficients, and conducting 20 independent experiments with 500-sample training sequences. Comparative analysis of three distinct step-size values is provided, demonstrating algorithm stability and convergence behavior through MATLAB-based simulations.

Principle Block Diagram of Linear Adaptive Equalizer

This study considers a linear adaptive equalizer's principle block diagram, referenced from the adaptive equalizer application schematic on page 275 of "Introduction to Modern Digital Signal Processing". The implementation utilizes the LMS (Least Mean Squares) algorithm to achieve adaptive equalization, featuring convergence curves of squared errors from single experiments and final filter coefficients. Key implementation aspects include a 500-sample training sequence length, 20 independent trials for statistical analysis, and performance comparison across three distinct step-size parameters to evaluate convergence characteristics.