一、报告题目

On Sampling Continuous-Time AWGN Channels

二、主讲人

韩光跃

三、报告时间

2022年7月5日10:00-12:00

四、报告地点

腾讯会议ID: 126 759 928

五、摘要

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.

六、主讲人简介

Guangyue Han received the B.S. and M.S. degrees in mathematics from Peking University, China, in 1997 and 2000, respectively, and the Ph.D. degree in mathematics from the University of Notre Dame, USA, in 2004. After three years with the Department of Mathematics, the University of British Columbia, Canada, he joined the Department of Mathematics, the University of Hong Kong, China, in 2007. His main research areas are coding and information theory.

七、主办单位

非线性期望前沿科学中心

数学与交叉科学研究中心