5G LDPC Linear Transformer for Channel Decoding
Mario Hernandez, Fernando Pinero

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 complexity rather than 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.
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Code & Models
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Taxonomy
TopicsError Correcting Code Techniques · Advanced Wireless Communication Techniques · PAPR reduction in OFDM
MethodsSoftmax · Residual Connection · Dropout · Absolute Position Encodings · Byte Pair Encoding · Linear Layer · Attention Is All You Need · Multi-Head Attention · Position-Wise Feed-Forward Layer · Label Smoothing
