A tail-calibrated soft-output GRAND decoder for finite-memory noise posteriors is introduced, with proven ML, unbiased missing-list estimation, and correlation-aware gains in simulations.
SOGRAND decoding of LDPC codes
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abstract
Long forward error correction codes are typically constructed by concatenating shorter component codes that are then decoded through iterative Soft-Input Soft-Output (SISO) of their components. The recently introduced Soft Output Guessing Random Additive Noise Decoding (SOGRAND) has been shown to enable accurate SISO component decoding for a broad range of component codes. Here we establish that by specializing its SISO computation to Single Parity Check codes, SOGRAND offers an alternative existing Check Node (CN) update for decoding Low Density Parity Check codes. Simulation results demonstrate similar or better decoding performance than Gallager's sum-product algorithm and norm-min-sum, while offering two distinct low complexity, hardware friendly CN update algorithms.
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Tail-Calibrated Soft-Output GRAND for Finite-Memory Noise-Effect Posteriors
A tail-calibrated soft-output GRAND decoder for finite-memory noise posteriors is introduced, with proven ML, unbiased missing-list estimation, and correlation-aware gains in simulations.