MATLAB Simulation of LMS Algorithm for Receiver-Side Adaptive Equalizer

This project presents a MATLAB simulation of the LMS algorithm implementation for adaptive equalizers at the receiver side. In communication systems, adaptive equalizers play a critical role in compensating for signal distortion and interference introduced during transmission. The LMS algorithm, based on the minimum mean square error criterion, dynamically updates equalizer coefficients to optimize signal recovery. The simulation implementation involves three key MATLAB coding phases: First, establishing a received signal model with configurable channel characteristics and noise parameters. Second, initializing equalizer coefficients and implementing the LMS update rule through iterative coefficient adjustment using the error signal between desired and actual outputs. The core algorithm follows the mathematical formulation: w(n+1) = w(n) + μ·e(n)·x(n), where μ represents the step size parameter critical for convergence stability. Finally, performance evaluation compares the simulated results with theoretical benchmarks through metrics like convergence curves and bit error rate (BER) analysis. The MATLAB code incorporates visualization functions to plot learning curves and constellation diagrams, demonstrating the equalizer's convergence behavior and interference suppression capabilities. This simulation provides practical insights into LMS-based adaptive equalization, serving as a valuable reference for communication system design and optimization. The modular code structure allows easy parameter modification for testing different channel conditions and algorithm variations.