paper

List-GRAND: A practical way to achieve Maximum Likelihood Decoding

arXiv:2109.12225 · doi:10.1109/TVLSI.2022.3223692

Abstract

Guessing Random Additive Noise Decoding (GRAND) is a recently proposed universal Maximum Likelihood (ML) decoder for short-length and high-rate linear block-codes. Soft-GRAND (SGRAND) is a prominent soft-input GRAND variant, outperforming the other GRAND variants in decoding performance; nevertheless, SGRAND is not suitable for parallel hardware implementation. Ordered Reliability Bits-GRAND (ORBGRAND) is another soft-input GRAND variant that is suitable for parallel hardware implementation, however it has lower decoding performance than SGRAND. In this paper, we propose List-GRAND (LGRAND), a technique for enhancing the decoding performance of ORBGRAND to match the ML decoding performance of SGRAND. Numerical simulation results show that LGRAND enhances ORBGRAND's decoding performance by dB for channel-codes of various classes at a target FER of . For linear block codes of length and different code-rates, LGRAND's VLSI implementation can achieve an average information throughput of Gbps. In comparison to ORBGRAND's VLSI implementation, the proposed LGRAND hardware has a area overhead.

arXiv commentThis article has been accepted for publication in IEEE Transactions on Very Large Scale Integration (VLSI) Systems. This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10.1109/TVLSI.2022.3223692

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