Experimental Report on Generating Gaussian White Noise Based on Generalized Mapping Theory

Abstract

This experiment aims to generate Gaussian white noise using a custom algorithm based on the Generalized Mapping Theory (GMT) and verify the statistical properties of the generated signal. Through the Central Limit Theorem (CLT), uniform distributions are transformed into a Gaussian distribution, and the Kolmogorov-Smirnov (KS) test and visualization techniques are employed to evaluate the quality of the generated noise. The results demonstrate that the generated signal closely approximates the target Gaussian distribution with a mean of 0 and a standard deviation of 1.