What Can Machine Learning Teach Us about Communications
Paper in proceeding, 2019

Rapid improvements in machine learning over the past decade are beginning to have far-reaching effects. For communications, engineers with limited domain expertise can now use off-the-shelf learning packages to design high-performance systems based on simulations. Prior to the current revolution in machine learning, the majority of communication engineers were quite aware that system parameters (such as filter coefficients) could be learned using stochastic gradient descent. It was not at all clear, however, that more complicated parts of the system architecture could be learned as well.
In this paper, we discuss the application of machine-learning techniques to two communications problems and focus on what can be learned from the resulting systems. We were pleasantly surprised that the observed gains in one example have a simple explanation that only became clear in hindsight. In essence, deep learning discovered a simple and effective strategy that had not been considered earlier.

Deep learning

Machine learning

Stochastic systems

Information theory


Mengke Lian

Duke University

Christian Häger

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Henry D. Pfister

Duke University

IEEE International Symposium on Information Theory - Proceedings

21578095 (ISSN)

Vol. 15 January 2019 8613331
978-153863599-5 (ISBN)

2018 IEEE Information Theory Workshop, ITW 2018
Guangzhou, China,

Coding for terabit-per-second fiber-optical communications (TERA)

European Commission (EC) (EC/H2020/749798), 2017-01-01 -- 2019-12-31.

Areas of Advance

Information and Communication Technology

Subject Categories


Communication Systems

Information Science

Electrical Engineering, Electronic Engineering, Information Engineering

Signal Processing

Computer Science



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3/2/2022 2