Decoding for Punctured Convolutional and Turbo Codes: A Deep Learning Solution for Protocols Compliance
This work addresses protocol compatibility issues in error correction for communication systems, representing an incremental improvement by adapting existing neural methods to handle punctured codes.
The paper tackled the challenge of adapting neural network-based decoding to punctured convolutional and Turbo codes for protocol compliance, proposing an LSTM-based decoder with puncturing-aware embedding and balanced training that outperformed conventional methods in simulations across AWGN and Rayleigh fading channels.
Neural network-based decoding methods show promise in enhancing error correction performance but face challenges with punctured codes. In particular, existing methods struggle to adapt to variable code rates or meet protocol compatibility requirements. This paper proposes a unified long short-term memory (LSTM)-based neural decoder for punctured convolutional and Turbo codes to address these challenges. The key component of the proposed LSTM-based neural decoder is puncturing-aware embedding, which integrates puncturing patterns directly into the neural network to enable seamless adaptation to different code rates. Moreover, a balanced bit error rate training strategy is designed to ensure the decoder's robustness across various code lengths, rates, and channels. In this way, the protocol compatibility requirement can be realized. Extensive simulations in both additive white Gaussian noise (AWGN) and Rayleigh fading channels demonstrate that the proposed neural decoder outperforms conventional decoding techniques, offering significant improvements in decoding accuracy and robustness.