Speaker:Han Guangyue, Department of Mathematics, the University of Hong Kong

Date:July. 5, 2022

Time:10:00-12:00

Location:Tencent Meeting,ID：126 759 928

Sponsor:Research Center for Mathematics and Interdisciplinary Sciences

Research Center for Nonlinear Expectation

Abstract:

For a continuous-time additive white Gaussian noise (AWGN) channel with possible feedback, it has been shown that as sampling gets infinitesimally fine, the mutual information of the associative discrete-time channels converges to that of the original continuous-time channel. We give in this paper more quantitative strengthenings of this result, which, among other implications, characterize how over-sampling approaches the true mutual information of a continuous-time Gaussian channel with bandwidth limit. The assumptions in our results are relatively mild. In particular, for the non-feedback case, compared to the Shannon-Nyquist sampling theorem, a widely used tool to connect continuous-time Gaussian channels to their discrete-time counterparts that requires the band-limitedness of the channel input, our results only require some integrability conditions on the power spectral density function of the input.

For more information, please visit:

https://www.view.sdu.edu.cn/info/1020/167320.htm