GRAND-EDGE: A Universal, Jamming-resilient Algorithm with Error-and-Erasure Decoding

TL;DR

GRAND-EDGE introduces a universal error-and-erasure decoding method that significantly enhances jamming resilience and reduces computational complexity in wireless communication error correction algorithms.

Contribution

The paper proposes the EDGE method to augment GRAND algorithms with erasure decoding, improving jamming resilience and efficiency.

Findings

EDGE reduces BLER by up to five orders of magnitude.

EDGE lowers computational complexity significantly.

ORBGRAND-EDGE outperforms OSD in BLER by up to three orders of magnitude.

Abstract

Random jammers that overpower transmitted signals are a practical concern for many wireless communication protocols. As such, wireless receivers must be able to cope with standard channel noise and jamming (intentional or unintentional). To address this challenge, we propose a novel method to augment the resilience of the recent family of universal error-correcting GRAND algorithms. This method, called Erasure Decoding by Gaussian Elimination (EDGE), impacts the syndrome check block and is applicable to any variant of GRAND. We show that the proposed EDGE method naturally reverts to the original syndrome check function in the absence of erasures caused by jamming. We demonstrate this by implementing and evaluating GRAND-EDGE and ORBGRAND-EDGE. Simulation results, using a Random Linear Code (RLC) with a code rate of $105/128$, show that the EDGE variants lower both the Block Error Rate (BLER) and the computational complexity by up to five order of magnitude compared to the original GRAND and ORBGRAND algorithms. We further compare ORBGRAND-EDGE to Ordered Statistics Decoding (OSD), and demonstrate an improvement of up to three orders of magnitude in the BLER.