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

Emil Bjornson, Pontus Giselsson

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskriftPeer review

Sammanfattning

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.
Originalspråkengelska
Artikelnummer9186132
Sidor (från-till)134-140
Antal sidor7
TidskriftIEEE Signal Processing Magazine
Volym37
Nummer5
DOI
StatusPublished - 2020

Ämnesklassifikation (UKÄ)

  • Datorseende och robotik (autonoma system)

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