Generation of Gaussian White Noise Using MATLAB for Editing and Simulation

The generation of Gaussian white noise serves as a fundamental concept in signal processing. To create such noise, MATLAB provides powerful tools for implementation and simulation. A critical characteristic of Gaussian white noise is its zero mean value, which must be maintained during generation. In MATLAB, this can be achieved using the randn function, which generates random numbers from a standard normal distribution (mean=0, variance=1). For creating Gaussian white noise with specific variance σ², we can scale the output: noise = σ * randn(1,N), where N represents the number of samples. The resulting sequence exhibits uniform power spectral density across all frequencies, mimicking ideal white noise characteristics. By employing such generated noise, researchers can simulate various signal processing scenarios, analyze system behaviors under different noise conditions, and validate signal processing algorithms. This approach provides valuable insights for applications in communications, audio processing, and statistical signal analysis. The awgn function (Additive White Gaussian Noise) offers a more direct implementation for adding noise to existing signals with specified SNR levels.