RLS Adaptive Channel Equalization Source Code and Learning Curve Plotting

This repository provides complete MATLAB source code for RLS (Recursive Least Squares) adaptive channel equalization with integrated learning curve plotting capabilities. The implementation utilizes the RLS algorithm to achieve adaptive channel equalization, which dynamically adjusts equalizer parameters in response to channel variations. The algorithm maintains a recursive update mechanism for weight coefficients using a forgetting factor, ensuring real-time adaptation to changing channel conditions and improved signal transmission quality. All source code is developed in MATLAB, leveraging its powerful matrix operations and signal processing toolbox functions. The implementation includes core components such as: - RLS filter initialization with optimal regularization parameters - Real-time weight update equations with exponential weighting - Mean square error calculation for performance monitoring - Adaptive step-size control for convergence optimization The learning curve plotting module enables comprehensive algorithm performance analysis by visualizing convergence behavior through metrics like: - Mean Square Error (MSE) progression over iterations - Weight vector convergence patterns - Steady-state error analysis across different channel scenarios - Comparative performance under various signal-to-noise ratios This complete package serves as both an educational resource for understanding RLS algorithm mechanics and a practical tool for evaluating adaptive equalization performance in communication systems. The modular code structure allows easy customization for specific channel models and performance requirements.