Fixed-Throughput GRAND with FIFO Scheduling

TL;DR

This paper introduces a FIFO scheduling architecture for GRAND decoding that ensures fixed throughput in real-time systems and improves block error rate by avoiding runtime constraints.

Contribution

It presents the first FIFO-based fixed-throughput GRAND architecture, enhancing decoding reliability and clarifying throughput metrics for hardware implementations.

Findings

FIFO scheduling achieves fixed throughput in GRAND decoding.

The proposed method improves block error rate over runtime-constrained approaches.

Achieving BLER comparable to unconstrained decoders requires lower throughput than commonly reported.

Abstract

Guessing random additive noise decoding (GRAND) is a code-agnostic decoding method that iteratively guesses the noise pattern affecting the received codeword. The number of noise sequences to test depends on the noise realization. Thus, GRAND exhibits random runtime which results in nondeterministic throughput. However, real-time systems must process the incoming data at a fixed rate, necessitating a fixed-throughput decoder in order to avoid losing data. We propose a first-in first-out (FIFO) scheduling architecture that enables a fixed throughput while improving the block error rate (BLER) compared to the common approach of imposing a maximum runtime constraint per received codeword. Moreover, we demonstrate that the average throughput metric of GRAND-based hardware implementations typically provided in the literature can be misleading as one needs to operate at approximately one order of magnitude lower throughput to achieve the BLER of an unconstrained decoder.