Two Applications of Deep Learning in the Physical Layer of Communication Systems [Lecture Notes]

Emil Bjornson, Pontus Giselsson

Research output: Contribution to journalArticlepeer-review

Abstract

Deep learning has proven itself to be a powerful tool to develop datadriven signal processing algorithms for challenging engineering problems. By learning the key features and characteristics of the input signals instead of requiring a human to first identify and model them, learned algorithms can beat many human-made algorithms. In particular, deep neural networks are capable of learning the complicated features of nature-made signals, such as photos and audio recordings, and using them for classification and decision making.
Original languageEnglish
Article number9186132
Pages (from-to)134-140
Number of pages7
JournalIEEE Signal Processing Magazine
Volume37
Issue number5
DOIs
Publication statusPublished - 2020

Subject classification (UKÄ)

  • Computer Vision and Robotics (Autonomous Systems)

Fingerprint

Dive into the research topics of 'Two Applications of Deep Learning in the Physical Layer of Communication Systems [Lecture Notes]'. Together they form a unique fingerprint.

Cite this