mlsanityData to 14 Oct 2026

5G LDPC Linear Transformer for Channel Decoding

arXiv 2501.14102 ↗First posted 23 Jan 2025cs.LG cs.IT math.ITcited by 0

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Abstract (arXiv)

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(n2) 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 19 references that are in the map. First posted on arXiv 23 Jan 2025.

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11 of 19 references.

References · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Linformer: Self-Attention with Linear Complexitycited by 1,474
  3. 2017An Introduction to Deep Learning for the Physical Layercited by 233
  4. 2022Sionna: An Open-Source Library for Next-Generation Physical Layer Researchcited by 145
  5. 2017Deep Learning-Based Communication Over the Aircited by 72
  6. 2016Learning to Decode Linear Codes Using Deep Learningcited by 61
  7. 2022Error Correction Code Transformercited by 47
  8. 2022Denoising Diffusion Error Correction Codescited by 24
  9. 2024Design of a Standard-Compliant Real-Time Neural Receiver for 5G NRcited by 12
  10. 2022Graph Neural Networks for Channel Decodingcited by 10
  11. 2024Optimizing Puncturing Patterns of 5G NR LDPC Codes for Few-Iteration Decodingcited by 1

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