LMS Algorithm Implementation for MVDR Adaptive Beamforming

This paper utilizes the Least Mean Squares (LMS) algorithm for MVDR (Minimum Variance Distortionless Response) adaptive beamforming implementation. The iteration count of the LMS algorithm significantly influences the final beamforming results. To systematically investigate this relationship, we propose the following experimental framework and code-based analysis:

1. Gradually increase the LMS iteration count while observing corresponding changes in beam pattern characteristics using visualization functions like polar plots or 3D radiation patterns.

2. Compare beamforming results across different iteration counts by implementing performance metrics such as Signal-to-Interference-plus-Noise Ratio (SINR) calculations and mainlobe/sidelobe analysis algorithms.

3. Investigate LMS algorithm performance under various parameter configurations (e.g., step size, convergence threshold) through Monte Carlo simulations to determine optimal iteration count using convergence analysis functions.

Through these code-driven experiments and parametric studies, we can quantitatively understand how LMS iteration count affects beamforming performance, providing critical insights for algorithm optimization and practical implementation guidelines.