What Can Machine Learning Teach Us about Communications
Paper i proceeding, 2018

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.

Författare

Mengke Lian

Duke University

Christian Häger

Chalmers, Elektroteknik, Kommunikationssystem, informationsteori och antenner, Kommunikationssystem

Henry D. Pfister

Information Theory Workshop (ITW)
Guangzhou, China,

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

Europeiska kommissionen (Horisont 2020), 2017-01-01 -- 2019-12-31.

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Telekommunikation

Kommunikationssystem

Systemvetenskap

Elektroteknik och elektronik

Signalbehandling

Datavetenskap (datalogi)

Mer information

Skapat

2019-01-20