Generating Gaussian Pseudorandom Noise from Binary Sequences with Guaranteed Statistical Properties

This work studies the theoretical framework to apply the Central Limit Theorem to generate Gaussian pseudorandom sequences from sums of binary sequences with good correlation properties, providing a relationship between the pseudorandomness of the input binary sequences and the statistical moments of the output Gaussian sequences.