A rate-compatible punctured Polar code decoding scheme based on deep learning

Wanqi Li, Qinghua Tian*, Yuqing Zhang, Feng Tian, Zhipei Li, Qi Zhang, Yongjun Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

In order to improve transmission reliability and flexible cooperation in optical communication, rate-compatible punctured Polar codes have become a research hotspot. Aiming at the problem that the traditional decoding performance and transmission efficiency is limited, based on deep learning, a rate-compatible punctured Polar code decoding scheme is studied. We use convolutional neural network model as the basic structure of rate-compatible Polar code decoder. The log likelihood ratio values of the received sequence are input into the decoder for training. Simulation results show that the proposed decoder outperforms the traditional punctured Polar code decoder under high signal-To-noise ratio.

Original languageEnglish
Title of host publicationICOCN 2022 - 20th International Conference on Optical Communications and Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665458986
DOIs
Publication statusPublished - 2022
Event20th International Conference on Optical Communications and Networks, ICOCN 2022 - Shenzhen, China
Duration: 12 Aug 202215 Aug 2022

Publication series

NameICOCN 2022 - 20th International Conference on Optical Communications and Networks

Conference

Conference20th International Conference on Optical Communications and Networks, ICOCN 2022
Country/TerritoryChina
CityShenzhen
Period12/08/2215/08/22

Keywords

  • Polar codes
  • deep learning
  • rare-compatible

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