Gaussian White Noise Generation Function

MATLAB provides specialized functions for generating Gaussian white noise, which can be customized through various parameter configurations to obtain desired noise characteristics. The study of white noise holds significant importance for the generation and application of random signals in signal processing. By examining white noise properties, we gain deeper understanding of signal processing fundamentals and communication principles. These functions typically implement algorithms using MATLAB's built-in randn() function, which generates normally distributed random numbers with zero mean and unit variance. Key parameters often include noise power spectral density, sampling frequency, and signal duration. For practical implementation, users can scale and shape the noise using mathematical operations like: noise_signal = sqrt(noise_power) * randn(1, N) where N represents the number of samples. White noise finds extensive applications in audio processing, image processing, and system identification, providing robust technical support for various engineering domains through its ideal flat spectrum characteristics and statistical properties.