T2: Generative and Discriminative AI Models for Physical Layer Communication Challenges

Chair: Andrea Tonello, University of Klagenfurt, Austria

Abstract: This tutorial focuses on machine learning for communications and more in detail on generative models to learn the signal statistics and enable a number of relevant applications in communications. An information theoretic approach is followed throughout the tutorial to explain the methods and the neural architectures presented. The theoretical concepts are accompanied by concrete application examples.

Learning the statistics of physical phenomena has been a long-time research objective. The advent of machine learning methods has offered effective tools to tackle such an objective in several data science domains. Some of those tools can be used in the domain of communication systems and networks. We emphasize that a distinction has to be made among data learning and signal learning. The former paradigm is typically applied to higher protocol layers, while the latter to the physical layer. Historically, stochastic models derived from the laws of physics have been exploited to describe the physical layer. From these models, transmission technology has been developed and performance analysis carried out. Nevertheless, this approach has shown some shortcomings in complex and uncertain environments.

Based on these preliminary considerations, in this tutorial, we will review basic concepts about the high order statistical description of random processes and conventional random signal generation methods. Then, recent generative and discriminative models capable of firstly learning the hidden/implicit distribution and then generating synthetic signals will be discussed. We will review the concept of copula and motivate the use of recently introduced segmented neural network architectures that operate in the uniform probability space. The application of such models to classic (but still open) problems in communications will be illustrated, including:
a) synthetic channel and noise modeling,
b) coding/decoding design in unknown channels,
c) channel capacity estimation.

In the above-mentioned problems, a key enabling component is the ability to estimate mutual information. This will lead us to the description of known and novel mutual information estimators. Their application will be considered to derive optimal decoding strategies with deep learning neural architectures obtained from an explainable mathematical formulation. Then, the joint design of the coding and decoding scheme aiming to achieve channel capacity will be considered. This will lead us to the discussion on autoencoders. Finally, the most ambitious goal of estimating capacity in unknown channels. This last goal rendered possible by the exploitation of cooperative methods that learn the capacity using neural mutual information estimation.

The tutorial will substantiate the theoretical aspects with several application examples not only in the wireless communication context but also in the less known power line communication domain (that has application in in-vehicle communications); the latter domain being perhaps more challenging giving the extremely complex nature of the channel and noise.

Chair’s Bio:

Andrea Tonello

Bio: Andrea Tonello is professor of embedded communication systems at the University of Klagenfurt, Austria. He has been associate professor at the University of Udine, Italy, technical manager at Bell Labs-Lucent Technologies, USA, and managing director of Bell Labs Italy where he was responsible for research activities on cellular technology. He is co-founder of the spinoff company WiTiKee and has a part-time associate professor post at the University of Udine, Italy. Dr. Tonello received the PhD from the University of Padova, Italy (2002). He was the recipient of several awards including: the Lucent Bell Labs Recognition of Excellence Award (1999), the RAENG (UK) Distinguished Visiting Fellowship (2010), the IEEE Vehicular Technology Society Distinguished Lecturer Award (2011-15), the IEEE Communications Society (ComSoc) Distinguished Lecturer Award (2018-19), the IEEE ComSoc TC-PLC Interdisciplinary and Research Award (2019), the IEEE ComSoc TCPLC Outstanding Service Award (2019), and the Chair of Excellence from UC3M (2019-20). He also received 10 best paper awards. He was/is associate editor of IEEE TVT, IEEE TCOM, IEEE ACCESS, IET Smart Grid, Elsevier Journal of Energy and Artificial Intelligence. He was the chair of the IEEE ComSoc Technical Committee on PLC (2014-18), and the director for industry outreach in the IEEE ComSoc board of governors (2020-21). and the IEEE ComSoc Technical Committee on Smart Grid Communications (2020-23).