Two Applications of Deep Learning in the Physical Layer of Communication Systems [Lecture Notes]
Bjornson, Emil and Giselsson, Pontus LU
(2020)
In IEEE Signal Processing Magazine
37(5).
p.134-140
- 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/66562a70-0271-4270-996b-a3e045a40aee
- author
- Bjornson, Emil
and Giselsson, Pontus
LU
- organization
- publishing date
- 2020
- type
- Contribution to journal
- publication status
- published
- subject
- in
- IEEE Signal Processing Magazine
- volume
- 37
- issue
- 5
- article number
- 9186132
- pages
- 7 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85091031828
- ISSN
- 1053-5888
- DOI
- 10.1109/MSP.2020.2996545
- language
- English
- LU publication?
- yes
- id
- 66562a70-0271-4270-996b-a3e045a40aee
- date added to LUP
- 2020-09-30 13:44:48
- date last changed
- 2023-11-20 11:28:00
@article{66562a70-0271-4270-996b-a3e045a40aee,
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.}},
author = {{Bjornson, Emil and Giselsson, Pontus}},
issn = {{1053-5888}},
language = {{eng}},
number = {{5}},
pages = {{134--140}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Signal Processing Magazine}},
title = {{Two Applications of Deep Learning in the Physical Layer of Communication Systems [Lecture Notes]}},
url = {{http://dx.doi.org/10.1109/MSP.2020.2996545}},
doi = {{10.1109/MSP.2020.2996545}},
volume = {{37}},
year = {{2020}},
}