Decoding for Punctured Convolutional and Turbo Codes: A Deep Learning Solution for Protocols Compliance

2025-02-21

AI Summary

Addressing the joint challenges of protocol compatibility and decoding performance for variable-rate punctured convolutional codes (PCCs) and Turbo codes, this paper proposes a unified LSTM-based joint decoding architecture. The method introduces two key innovations: (1) a learnable puncturing pattern embedding mechanism that explicitly encodes dynamic puncturing structures; and (2) a BER-balanced loss function combined with multi-channel joint training to enhance generalization across varying code rates, channel conditions (AWGN and Rayleigh fading), and code types. Experimental results demonstrate that the proposed decoder achieves 30–50% lower bit error rate (BER) compared to conventional BCJR and iterative Turbo decoders, while maintaining real-time computational efficiency. This work establishes a new paradigm for protocol-adaptive physical-layer decoding.

Technology Category

• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

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• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Neural network-based decoding methods have shown promise in enhancing error correction performance, but traditional approaches struggle with the challenges posed by punctured codes. In particular, these methods fail to address the complexities of variable code rates and the need for protocol compatibility. This paper presents a unified Long Short-Term Memory (LSTM)-based decoding architecture specifically designed to overcome these challenges. The proposed method unifies punctured convolutional and Turbo codes. A puncture embedding mechanism integrates puncturing patterns directly into the network, enabling seamless adaptation to varying code rates, while balanced bit error rate training ensures robustness across different code lengths, rates, and channels, maintaining protocol flexibility. Extensive simulations in Additive White Gaussian Noise and Rayleigh fading channels demonstrate that the proposed approach outperforms conventional decoding techniques, providing significant improvements in decoding accuracy and robustness. These results underscore the potential of LSTM-based decoding as a promising solution for next-generation artificial intelligence powered communication systems.

Problem

• Enhance error correction for punctured codes
• Address variable code rates and protocol compatibility
• Unify convolutional and Turbo codes decoding

Innovation

• LSTM-based decoding architecture
• Puncture embedding mechanism
• Balanced bit error rate training