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Deep extended feedback codes
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Authors: Anahid Robert Safavi, Alberto G. Perotti, Branislav M. Popovic, Mahdi Boloursaz Mashhadi, Deniz Gündüz
Status: Final
Date of publication: 13 September 2021
Published in: ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 6 - Wireless communication systems in beyond 5G era, Pages 33-41
Article DOI : https://doi.org/10.52953/SNLM1743
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 | Abstract:
A new Deep Neural Network (DNN)-based error correction encoder architecture for channels with feedback, called Deep Extended Feedback (DEF), is presented in this paper. The encoder in the DEF architecture transmits an information message followed by a sequence of parity symbols which are generated based on the message as well as the observations of the past forward channel outputs sent to the transmitter through a feedback channel. DEF codes generalize Deepcode in several ways: parity symbols are generated based on forward channel output observations over longer time intervals in order to provide better error correction capability; and high-order modulation formats are deployed in the encoder so as to achieve increased spectral efficiency. |
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Keywords: Deep learning, error correction, feedback, ultra-reliable
Rights: © International Telecommunication Union, available under the CC BY-NC-ND 3.0 IGO license.
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