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DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning
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Progress in designing channel codes has been driven by human ingenuity and, fittingly, has been sporadic. Polar codes, developed on the foundation of Arikan's polarization kernel, represent the latest breakthrough in coding theory and have emerged as the state-of-the-art error-correction code for short-to-medium block length regimes. In an effort to automate the invention of good channel codes, especially in this regime, we explore a novel, non-linear generalization of Polar codes, which we call DeepPolar codes. DeepPolar codes extend the conventional Polar coding framework by utilizing a larger kernel size and parameterizing these kernels and matched decoders through neural networks. Our results demonstrate that these data-driven codes effectively leverage the benefits of a larger kernel size, resulting in enhanced reliability when compared to both existing neural codes and conventional Polar codes.
Forward citations
Cited by 3 Pith papers
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Actions Speak Louder Than Words: Rate-Reward Trade-off in Markov Decision Processes
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DeepPolar+: Breaking the BER-BLER Trade-off with Self-Attention and SMART (SNR-MAtched Redundancy Technique) decoding
An attention-based neural polar decoder with a block-level loss improves simulated BER and BLER for a (256,37) polar code, and a CRC-guided ensemble variant reports further gains.
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