5G LDPC Linear Transformer for Channel Decoding

TL;DR

This paper presents a novel, scalable transformer-based decoder for 5G LDPC codes with linear complexity, matching or surpassing traditional belief propagation in performance and efficiency.

Contribution

Introduces a fully differentiable linear-time transformer decoder for 5G LDPC decoding, improving scalability and performance over existing methods.

Findings

Achieves bit error rate comparable to regular Transformer decoders.

Surpasses one iteration of belief propagation in performance.

Offers competitive decoding time for large block codes.

Abstract

This work introduces a novel, fully differentiable linear-time complexity transformer decoder and a transformer decoder to correct 5G New Radio (NR) LDPC. We propose a scalable approach to decode linear block codes with $O(n)$ complexity rather than $O(n^2)$ for regular transformers. The architectures' performances are compared to Belief Propagation (BP), the production-level decoding algorithm used for 5G New Radio (NR) LDPC codes. We achieve bit error rate performance that matches a regular Transformer decoder and surpases one iteration BP, also achieving competitive time performance against BP, even for larger block codes. We utilize Sionna, Nvidia's 5G & 6G physical layer research software, for reproducible results.