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About this paper
Shy-el Cohen, Yoni Choukroun, Eliya Nachmani
A hybrid Mamba-Transformer architecture is introduced for decoding binary linear block codes, achieving up to 18% relative improvement in Bit Error Rate for BCH codes compared to prior neural decoders while offering faster inference times. This approach integrates Mamba's efficient sequential modeling with Transformer's global attention to improve both computational efficiency and decoding accuracy across various code families.
Key insights
- Mamba and Transformer layers learn complementary aspects of the code structure: Mamba focuses on local sequential patterns, while Transformers capture global dependencies.
- The progressive layer-wise processing allows for the refinement of error estimates through the network depth, enabling multiple supervision points.
- Integrating code structure knowledge through masking in Mamba blocks and Hamming-Distance-Based Parity-Check Self-Attention (HPSA) in Transformer blocks significantly enhances decoding efficiency and effectiveness.
Summary by alphaXiv.
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