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Rate Compatible LDPC Neural Decoding Network: A Multi-Task Learning Approach
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DOI：10.1109/TVT.2023.3344127.png)
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Abstract
En 中文
Deep learning based decoding networks have shown significant improvement in decoding low density parity check (LDPC) codes, but the neural decoders are limited by rate-matching operations such as puncturing or extending, thus needing to train multiple decoders with different code rates for a variety of channel conditions. In this correspondence, we propose a Multi-Task Learning based rate-compatible LDPC decoding network, which utilizes the structure of raptor-like LDPC codes and can deal with multiple code rates. In the proposed network, different portions of parameters are activated to deal with distinct code rates, which leads to parameter sharing among tasks. Numerical experiments demonstrate the effectiveness of the proposed method. Training the specially designed network under multiple code rates makes the decoder compatible with multiple code rates without sacrificing frame error rate performance.
Keywords:
Codes
Decoding
Iterative decoding
Task analysis
Training
Matrix decomposition
Multitasking
Rate-compatible low density parity check (LDPC) codes
5G
parameter-sharing
neural LDPC decoder
Journal
IF:
7.1
Papers:
1.8W
Citations:
6.6W
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Cited Papers
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