Two Implementation Methods of Gaussian White Noise in MATLAB

In this article, we will conduct an in-depth exploration of two MATLAB implementation methods for Gaussian white noise. Both approaches have been experimentally validated to ensure their feasibility and accuracy. First, we will introduce the first implementation method, which utilizes MATLAB's built-in functions to generate Gaussian white noise. This approach leverages functions like randn() which generates arrays of random numbers from a standard normal distribution, making it efficient for creating noise sequences with zero mean and unit variance. Then, we will discuss the second implementation method, which employs custom code to generate Gaussian white noise. This method typically involves implementing algorithms such as the Box-Muller transform or using cumulative distribution functions to create normally distributed random variables from uniform random numbers. When using this implementation approach, special attention must be paid to ensuring code correctness and effectiveness to guarantee that the generated Gaussian white noise meets our expectations and requirements. Through this article, readers will gain understanding of two distinct MATLAB implementation methods for Gaussian white noise and master how to use them to generate high-quality noise signals. Furthermore, we will provide experimental results and detailed explanations to help readers better comprehend the usage, advantages, and limitations of these implementation approaches. In summary, this article aims to provide readers with a comprehensive guide to Gaussian white noise implementation methods and assist them in effectively applying these techniques within the MATLAB environment.