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The Negative BER Loss Function for Deep Learning Decoders
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AI
DOI：10.1109/LCOMM.2022.3178959.png)
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摘要
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After certain training epochs of the deep learning decoders, the binary cross entropy (BCE) loss function ignores the training of some unsuccessful decoded information bits, thereby degrading the training efficiency. In this letter, we propose the negative bit error rate (NBER) loss function to increase the training concentration degree on the unsuccessful decoded information bits by modifying the gradient on different information bits, so that the training efficiency and decoding performance can be improved. The simulation results show that NBER could achieve significant performance improvement over BCE both in the deep neural network decoders and the weighted belief propagation decoders.
Keyword:
Decoding
Training
Deep learning
Maximum likelihood decoding
Bit error rate
Neural networks
Belief propagation
Deep learning decoders
loss function
error correcting codes
training concentration degree
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
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