| Title | Local Constraint-Based Ordered Statistics Decoding for Short Block Codes |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Wang, Yiwen, Liang, Jifan, Ma, Xiao |
| Conference Name | 2022 IEEE Information Theory Workshop (ITW) |
| Keywords | block codes, coding theory, Complexity theory, composability, compositionality, Conferences, cryptography, Decoding, List Viterbi decoding, Metrics, ordered statistics decoding, pubcrawl, resilience, Resiliency, security, short block codes, simulation, Upper bound, URLLC, Viterbi algorithm |
| Abstract | In this paper, we propose a new ordered statistics decoding (OSD) for linear block codes, which is referred to as local constraint-based OSD (LC-OSD). Distinguished from the conventional OSD, which chooses the most reliable basis (MRB) for re-encoding, the LC-OSD chooses an extended MRB on which local constraints are naturally imposed. A list of candidate codewords is then generated by performing a serial list Viterbi algorithm (SLVA) over the trellis specified with the local constraints. To terminate early the SLVA for complexity reduction, we present a simple criterion which monitors the ratio of the bound on the likelihood of the unexplored candidate codewords to the sum of the hard-decision vector's likelihood and the up-to-date optimal candidate's likelihood. Simulation results show that the LC-OSD can have a much less number of test patterns than that of the conventional OSD but cause negligible performance loss. Comparisons with other complexity-reduced OSDs are also conducted, showing the advantages of the LC-OSD in terms of complexity. |
| DOI | 10.1109/ITW54588.2022.9965916 |
| Citation Key | wang_local_2022 |
Local Constraint-Based Ordered Statistics Decoding for Short Block Codes