A fixed latency ORBGRAND decoder architecture with LUT-aided error-pattern scheduling

TL;DR

This paper introduces a fixed latency ORBGRAND decoder architecture with an improved error-pattern scheduling, achieving high throughput and low latency for practical, latency-constrained decoding applications.

Contribution

It proposes a novel pattern schedule for ORBGRAND and a fixed latency decoder architecture that significantly improves worst-case performance and efficiency.

Findings

>0.5 dB gain over standard schedule at BER ≤ 10^{-5}

Decodes 128-length code at 79.21 Gb/s with 58.49 ns latency

Outperforms worst-case and many best-case decoders

Abstract

Guessing Random Additive Noise Decoding (GRAND) is a universal decoding algorithm that has been recently proposed as a practical way to perform maximum likelihood decoding. It generates a sequence of possible error patterns and applies them to the received vector, checking if the result is a valid codeword. Ordered reliability bits GRAND (ORBGRAND) improves on GRAND by considering soft information received from the channel. Both GRAND and ORBGRAND have been implemented in hardware, focusing on average performance, sacrificing worst case throughput and latency. In this work, an improved pattern schedule for ORBGRAND is proposed. It provides $>0.5$ dB gain over the standard schedule at a block error rate $\le 10^{-5}$, and outperforms more complex GRAND flavors with a fraction of the complexity. The proposed schedule is used within a novel code-agnositic decoder architecture: the decoder guarantees fixed high throughput and low latency, making it attractive for latency-constrained applications. It outperforms the worst-case performance of decoders by orders of magnitude, and outperforms many best-case figures. Decoding a code of length 128, it achieves a throughput of $79.21$ Gb/s with $58.49$ ns latency, yielding better energy efficiency and comparable area efficiency with respect to the state of the art.