Prof. Luca Barletta

Politecnico di Milano

Abstract:

It is well-known that the Gaussian distribution is the capacity-achieving input distribution for the AWGN channel with an average power constraint. However, what happens when we impose different constraints on the channel? In this talk, we will explore this question by examining the capacity-achieving distribution under different constraints. We will introduce new techniques for studying capacity-achieving input distributions and apply them to two important communication channels: i) the classical AWGN channel under a peak power constraint and ii) the Poisson channel under an amplitude constraint. The Poisson distribution is particularly well-suited to modeling low-intensity direct-detection optical communication channels, which are used in fiber-optic communication systems and optical wireless communication.

Biography:

Luca Barletta (Member, IEEE) received the M.S. Degree (cum laude) in telecommunications engineering and the Ph.D. degree in information engineering from Politecnico di Milano, Milano, Italy, in 2007 and 2011, respectively. In 2012, he was a visiting researcher at Bell Labs, Alcatel-Lucent, Holmdel, NJ, USA, working on long-haul fiber-optic communications. From 2012 to 2015 he was a post-doctoral researcher at the Institute for Advanced Study and at the Institute for Communications Engineering, Technische Universität München, Munich, Germany, working on capacity for continuous-time phase-noise channels. Since 2016, he has been with the Politecnico di Milano, where he is currently an Associate Professor, working in the broad field of information and communication theory. In 2020, he received the IEEE Communications Society Charles Kao Award for the paper “Machine-Learning Method for Quality of Transmission Prediction of Unestablished Lightpaths”, appeared on the IEEE/OSA Journal of Optical Communications and Networking, Vol. 10, No. 2.