DeepPolar+: Breaking the BER-BLER Trade-off with Self-Attention and SMART (SNR-MAtched Redundancy Technique) decoding

2025-06-11

AI Summary

DeepPolar codes suffer from a fundamental BER–BLER trade-off—exhibiting excellent bit error rate (BER) performance but suboptimal block error rate (BLER)—hindering practical deployment. To address this, we propose an end-to-end trainable enhancement framework that systematically breaks the BER–BLER trade-off for the first time. Our method introduces three key innovations: (1) a multi-head self-attention–based enhanced decoder; (2) a structured loss function jointly optimizing bit-level and block-level objectives; and (3) SNR-matched adaptive redundancy decoding (SMART), integrating CRC-aided detection with a neural–classical error-correction synergy architecture. Evaluated on the (256,37) polar code, our approach simultaneously outperforms both DeepPolar and successive cancellation (SC) decoders in both BER and BLER, achieves faster convergence, exhibits strong generalization across channel conditions, and maintains robustness over a wide SNR range.

Technology Category

Machine Learning: Deep Generative Models & AutoencodersSearch and Optimization: Learning to SearchNatural Language Processing: Learning & Optimization for NLP

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsSecurity and Privacy: Privacy-enhancing technologies

Abstract

DeepPolar codes have recently emerged as a promising approach for channel coding, demonstrating superior bit error rate (BER) performance compared to conventional polar codes. Despite their excellent BER characteristics, these codes exhibit suboptimal block error rate (BLER) performance, creating a fundamental BER-BLER trade-off that severely limits their practical deployment in communication systems. This paper introduces DeepPolar+, an enhanced neural polar coding framework that systematically eliminates this BER-BLER trade-off by simultaneously improving BLER performance while maintaining the superior BER characteristics of DeepPolar codes. Our approach achieves this breakthrough through three key innovations: (1) an attention-enhanced decoder architecture that leverages multi-head self-attention mechanisms to capture complex dependencies between bit positions, (2) a structured loss function that jointly optimizes for both bit-level accuracy and block-level reliability, and (3) an adaptive SNR-Matched Redundancy Technique (SMART) for decoding DeepPolar+ code (DP+SMART decoder) that combines specialized models with CRC verification for robust performance across diverse channel conditions. For a (256,37) code configuration, DeepPolar+ demonstrates notable improvements in both BER and BLER performance compared to conventional successive cancellation decoding and DeepPolar, while achieving remarkably faster convergence through improved architecture and optimization strategies. The DeepPolar+SMART variant further amplifies these dual improvements, delivering significant gains in both error rate metrics over existing approaches. DeepPolar+ effectively bridges the gap between theoretical potential and practical implementation of neural polar codes, offering a viable path forward for next-generation error correction systems.

Problem

• Eliminates BER-BLER trade-off in DeepPolar codes
• Improves BLER while maintaining superior BER performance
• Enhances error correction for practical communication systems

Innovation

• Attention-enhanced decoder with self-attention mechanisms
• Structured loss for bit and block-level optimization
• SMART decoding with SNR-matched redundancy