Adaptive Beamforming Algorithm Based on LMS (Least Mean Squares) Criterion

This article introduces the adaptive beamforming algorithm based on the Least Mean Squares (LMS) criterion and provides MATLAB simulation examples. Adaptive beamforming is a crucial technique in signal processing and communication systems that enhances desired signals and suppresses interference by dynamically adjusting weight coefficients in antenna arrays. The article details the theoretical foundation of the LMS algorithm, explains its implementation steps including weight vector update equations and convergence parameters, and demonstrates its performance through MATLAB simulations. Key MATLAB functions and implementation approaches such as iterative weight adaptation and signal covariance estimation are discussed. Readers can use the provided simulation code to understand fundamental concepts and practical applications of adaptive beamforming, including real-time interference cancellation and directional signal enhancement scenarios. The simulation examples clearly illustrate the algorithm's convergence behavior and beam pattern formation under various signal conditions.