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arXiv · 2109.12225

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

Syed Mohsin Abbas·Marwan Jalaleddine·Warren J. Gross
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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 $0.5-0.75$ dB for channel-codes of various classes at a target FER of $10^{-7}$. For linear block codes of length $127/128$ and different code-rates, LGRAND's VLSI implementation can achieve an average information throughput of $47.27-51.36$ Gbps. In comparison to ORBGRAND's VLSI implementation, the proposed LGRAND hardware has a $4.84\%$ area overhead.

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BibTeXRIS

Syed Mohsin Abbas, Marwan Jalaleddine, Warren J. Gross. 2021-09-24. List-GRAND: A practical way to achieve Maximum Likelihood Decoding. https://doi.org/10.1109/tvlsi.2022.3223692

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