Ordered Reliability Bits Guessing Random Additive Noise Decoding

TL;DR

This paper introduces ORBGRAND, a universal soft-detection decoding algorithm that efficiently and accurately decodes various moderate redundancy codes, promising improved performance for ultra-reliable low-latency communications.

Contribution

The paper presents ORBGRAND, a novel soft-detection decoding method compatible with multiple code types, offering high accuracy and hardware efficiency for URLLC applications.

Findings

ORBGRAND outperforms CA-SCL in block error rate for tested codes.

It maintains high parallelizability suitable for hardware implementation.

Provides reliable decoding for BCH, CA-Polar, and RLC codes.

Abstract

Error correction techniques traditionally focus on the co-design of restricted code-structures in tandem with code-specific decoders that are computationally efficient when decoding long codes in hardware. Modern applications are, however, driving demand for ultra-reliable low-latency communications (URLLC), rekindling interest in the performance of shorter, higher-rate error correcting codes, and raising the possibility of revisiting universal, code-agnostic decoders. To that end, here we introduce a soft-detection variant of Guessing Random Additive Noise Decoding (GRAND) called Ordered Reliability Bits GRAND that can accurately decode any moderate redundancy block-code. It is designed with efficient circuit implementation in mind, and determines accurate decodings while retaining the original hard detection GRAND algorithm's suitability for a highly parallelized implementation in hardware. ORBGRAND is shown to provide excellent soft decision block error performance for codes of distinct classes (BCH, CA-Polar and RLC) with modest complexity, while providing better block error rate performance than CA-SCL, a state of the art soft detection CA-Polar decoder. ORBGRAND offers the possibility of an accurate, energy efficient soft detection decoder suitable for delivering URLLC in a single hardware realization.