Algorithm Simulation of MMSE and SNR

This simulation implements MMSE and SNR algorithms to obtain optimal weights for adaptive filtering, achieving anti-interference performance. The implementation involves signal processing through optimization algorithms to enhance system performance and ensure effective response to various interference scenarios. The simulation demonstrates weight optimization through iterative algorithms like LMS (Least Mean Squares) or RLS (Recursive Least Squares), where the weight vector is updated using error minimization techniques. The MMSE approach calculates optimal weights by minimizing the mean square error between desired and actual signals, while SNR optimization focuses on maximizing signal quality metrics. The system adapts to interference patterns through real-time weight adjustments using gradient descent or matrix inversion methods. This methodology provides deeper understanding of adaptive filtering applications for improving communication system reliability and performance under noisy conditions.