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A low-complexity dual trellis decoding algorithm for high-rate convolutional codes

Vinh Hoang Son Le 1, 2 Charbel Abdel Nour 1, 2 Catherine Douillard 1, 2 Emmanuel Boutillon 3, 4
2 Lab-STICC_IMTA_CACS_IAS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
4 Lab-STICC_UBS_CACS_IAS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : Decoding using the dual trellis is considered as a potential technique to increase the throughput of soft-input soft-output decoders for high coding rate convolutional codes. However, the dual Log-MAP algorithm suffers from a high decoding complexity. More specifically, the source of complexity comes from the soft-output unit, which has to handle a high number of extrinsic values in parallel. In this paper, we present a new low-complexity sub-optimal decoding algorithm using the dual trellis, namely the dual Max-Log-MAP algorithm, suited for high coding rate convolutional codes. A complexity analysis and simulation results are provided to compare the dual Max-Log-MAP and the dual Log-MAP algorithms. Despite a minor loss of about 0.2 dB in performance, the dual Max-Log-MAP algorithm significantly reduces the decoder complexity and makes it a first-choice algorithm for high-throughput high-rate decoding of convolutional and turbo codes.
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Submitted on : Monday, March 9, 2020 - 10:24:34 AM
Last modification on : Tuesday, September 8, 2020 - 4:42:23 PM
Long-term archiving on: : Wednesday, June 10, 2020 - 1:47:39 PM

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Vinh Hoang Son Le, Charbel Abdel Nour, Catherine Douillard, Emmanuel Boutillon. A low-complexity dual trellis decoding algorithm for high-rate convolutional codes. WCNC 2020 : IEEE Wireless Communications and Networking Conference, May 2020, Seoul, South Korea. ⟨10.1109/WCNC45663.2020.9120466⟩. ⟨hal-02502291⟩

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