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Nested Construction of Polar Codes via Transformers
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Tailoring polar code construction for decoding algorithms beyond successive cancellation has remained a topic of significant interest in the field. However, despite the inherent nested structure of polar codes, the use of sequence models in polar code construction is understudied. In this work, we propose using a sequence modeling framework to iteratively construct a polar code for any given length and rate under various channel conditions. Simulations show that polar codes designed via sequential modeling using transformers outperform both 5G-NR sequence and Density Evolution based approaches for both AWGN and Rayleigh fading channels.
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Cited by 1 Pith paper
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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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