SOGRAND decoding of LDPC codes

2026-07-04

AI Summary

This work addresses the trade-off between performance and complexity in soft-input soft-output (SISO) decoding of low-density parity-check (LDPC) codes by introducing, for the first time, the Soft Output Guessing Random Additive Noise Decoding (SOGRAND) framework into check-node updating. Specifically tailored for single parity-check codes, two novel low-complexity and hardware-friendly decoding algorithms are proposed. These methods achieve decoding performance comparable to or better than that of the Gallager and normalized min-sum algorithms while significantly reducing computational complexity, thereby offering an efficient and implementation-friendly solution.

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.

Problem

• LDPC codes
• Soft-Input Soft-Output decoding
• Check Node update
• low complexity
• hardware friendly

Innovation

• SOGRAND
• LDPC codes
• Soft-Input Soft-Output (SISO)
• Check Node update
• low-complexity decoding