We study the problem of common randomness (CR) generation in the basic two-party communication setting in which the sender and the receiver aim to agree on a common random variable with high probability by observing independent and identically distributed (i.i.d.) samples of correlated Gaussian sources and while communicating as little as possible over a noisy memoryless channel. We completely solve the problem by giving a single-letter characterization of the CR capacity for the proposed model and by providing a rigorous proof of it. Interestingly, we prove that the CR capacity is infinite when the Gaussian sources are perfectly correlated.
@article{arxiv.2201.11078,
title = {Common Randomness Generation from Gaussian Sources},
author = {Wafa Labidi and Rami Ezzine and Christian Deppe and Holger Boche},
journal= {arXiv preprint arXiv:2201.11078},
year = {2022}
}