A Delay-Efficient Deep Learning Approach for Lossless Turbo Source Coding

Manouchehri S., Haghighat J., Eslami M., Hamouda W.

IEEE Transactions on Vehicular Technology, vol.71, no.6, pp.6704-6709, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 71 Issue: 6
  • Publication Date: 2022
  • Doi Number: 10.1109/tvt.2022.3155545
  • Journal Name: IEEE Transactions on Vehicular Technology
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.6704-6709
  • Keywords: Encoding, Iterative decoding, Delays, Decoding, Source coding, Turbo codes, Neurons, Data compression, delay, neural networks, turbo code
  • TED University Affiliated: No


Lossless turbo source coding with decremental/incremental redundancy is a variable-length source coding scheme which employs turbo codes for data compression. Although the scheme offers low compression rates and lends itself to joint source-channel coding, it suffers from a large delay in the encoding phase. The delay is imposed by several tentative encoding-decoding procedures performed at the encoder to search for the minimum compression length. In this work, we apply machine learning to provide a highly accurate estimate of the proper compression length. The encoder starts its search from this estimated length, thus the delay of turbo source coding will decrease considerably. The preliminary results show a four-fold reduction in the encoding delay at the expense of a negligible increase in the compression rate.