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Self-Corrected Belief-Propagation Decoder for Source Coding with Unknown Source Statistics

Elsa Dupraz 1, 2 Mohamed Yaoumi 1 
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance : UMR6285
Abstract : This paper describes a practical Slepian-Wolf source coding scheme based on Low Density Parity Check (LDPC) codes. It considers the realistic setup where the parameters of the statistical model between the source and the side information are unknown. A novel Self-Corrected Belief-Propagation (SC-BP) algorithm is proposed in order to make the coding scheme robust to incorrect model parameters by introducing some memory inside the LDPC decoder. A Two Dimensional Density Evolution (2D-DE) analysis is then developed to predict the theoretical performance of the SC-BP decoder. Both the 2D-DE analysis and Monte-Carlo simulations confirm the robustness of the SC-BP decoder. The proposed solution allows for an important complexity reduction and shows a performance very close to existing methods which jointly estimate the model parameters and the source sequence.
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Submitted on : Tuesday, September 7, 2021 - 4:57:45 PM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM
Long-term archiving on: : Wednesday, December 8, 2021 - 8:08:40 PM


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Elsa Dupraz, Mohamed Yaoumi. Self-Corrected Belief-Propagation Decoder for Source Coding with Unknown Source Statistics. IEEE Communications Letters, 2021, Volume 25 (Issue: 7), pp.2133 - 2137. ⟨10.1109/LCOMM.2021.3075110⟩. ⟨hal-03337231⟩



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