Conference paper Open Access

A Low-Complexity Dual Trellis Decoding Algorithm for High-Rate Convolutional Codes

Son Le, V.; Abdel Nour, C.; Douillard, C.; Boutillon, E.


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3971729", 
  "language": "eng", 
  "title": "A Low-Complexity Dual Trellis Decoding Algorithm for High-Rate Convolutional Codes", 
  "issued": {
    "date-parts": [
      [
        2020, 
        8, 
        4
      ]
    ]
  }, 
  "abstract": "<p>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.</p>", 
  "author": [
    {
      "family": "Son Le, V."
    }, 
    {
      "family": "Abdel Nour, C."
    }, 
    {
      "family": "Douillard, C."
    }, 
    {
      "family": "Boutillon, E."
    }
  ], 
  "id": "3971729", 
  "event-place": "Virtual Conference", 
  "type": "paper-conference", 
  "event": "IEEE Wireless Communications and Networking Conference 2020 (2020 IEEE WCNC)"
}
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