cfaed Seminar Series

cfaed Seminar Series

Dr. Alessio Zappone , LANEAS, Centrale Supélec, Gif-sur-Yvette, France

Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?

03.04.2019 (Wednesday) - 11.04.2019 (Thursday) , 10:00 - 12:00
FAL 07/08, Falkenbrunnen , Würzburger Str. 35 , 01187 Dresden

Recently, deep learning has received significant attention as a technique
to design and optimize wireless communication systems and networks. The usual
approach to use deep learning consists of acquiring large amount of empirical data
about the system behavior and employ it for performance optimization. We believe,
however, that the application of deep learning to communication networks design
and optimization offersmore possibilities. As opposed to other fields of science, such
as image classification and speech recognition, mathematical models for communication
networks optimization are very often available, even though they may be
simplified and inaccurate.We believe that this a priori expert knowledge, which has
been acquired over decades of intense research, cannot be dismissed and ignored.
In this tutorial, in particular, we explore approaches that capitalize on the availability
of (possibly simplified or inaccurate) theoretical models, in order to reduce
the amount of empirical data to use and the complexity of training artificial neural
networks (ANNs). We concretely show, with the aid of some examples, that synergistically
combining prior expert knowledge based on analytical models and datadriven
methods constitutes a suitable approach towards the design and optimization
of communication systems and networks with the aid of deep learning based on
ANNs. The course is structured into three main parts:
• Deep learning by artificial neural networks: machine learning fundamentals, feedforward
neural networks, deep reinforcement learning, deep transfer learning
• Machine learning fundamentals: feedforward neural networks, deep reinforcement
learning, deep transfer learning
• Model-based design of wireless networks: performance metrics, convex optimization
techniques, sequential optimization techniques
• Model-aided deep learning for wireless communication design: learning to optimize,
applications to interference networks, applications to massive MIMO networks,
applications to energy-harvesting in wireless networks

The tutorial will be mostly based on the following reference:
A. Zappone, M. Di Renzo, M. Debbah, “Wireless Networks Design in the Era of Deep
Learning: Model-Based, AI-Based, or Both?”, submitted to IEEE Transactions on
Communications, available at

Bio: Alessio Zappone received his Master degree in Telecommunication Engineering
and his and Ph.D. degree in Electrical and Information Engineering from the
University of Cassino and Southern Lazio, Cassino, Italy, in 2007 and 2011, respectively.
In 2009 he spent a semester at the Technische Universitaet Dresden, Dresden,
Germany, as a visiting Ph.D. student. In 2011/2012 he worked with Consorzio Nazionale
Interuniversitario per le Telecomunicazioni (CNIT), in the framework of the
EU FP7 project TREND. From 2012 to 2016 he has been the principal investigator
of the project CEMRIN, carried out at the Chair of Communication Theory of the
Technische Universitaet Dresden, and funded by the German Research Foundation
(DFG). In 2014 he was the recipient of a Newcom mobility grant. In 2017 he has
been the recipient of the Marie Curie Individual Fellowship grant BESMART, carried
out at the Large Networks and Systems Group, CentraleSupeléc, Gif-sur-Yvette,
France. His research interests lie in the area of communication theory and signal
processing, with main focus on optimization techniques for resource allocation and
energy efficiency. Alessio serves as associate editor for the IEEE Signal Processing
Letters and has served as associate editor for the IEEE Journal on Selected Areas
on Communications (Special Issue on Energy-Efficient Techniques for 5G Wireless
Communication Systems). He is the first author of the monograph “Energy efficiency
in wireless networks via fractional programming theory”, appeared in the 2015
issue of Foundations and Trends in Communications and Information Theory. He
is an IEEE Senior Member and a exemplary reviewer of IEEE Transactions on Communications
and Transactions on Wireless Communications.

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