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.

### Citation Style Language JSON Export

{
"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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