Low-complexity Recurrent Neural Network-based Polar Decoder with Weight Quantization Mechanism

Polar codes have drawn much attention and been adopted in 5G New Radio (NR) due to their capacity-achieving performance. Recently, as the emerging deep learning (DL) technique has breakthrough achievements in many fields, neural network decoder was proposed to obtain faster convergence and better pe...

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Vydáno v:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 1413 - 1417
Hlavní autoři: Teng, Chieh-Fang, Wu, Chen-Hsi Derek, Kuan-Shiuan Ho, Andrew, Wu, An-Yeu Andy
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2019
Témata:
Belief propagation
Communication systems
Decoding
Deep learning
New Radio
Polar codes
Quantization (signal)
recurrent neural network
Recurrent neural networks
Speech coding
Speech processing
weight quantization
ISSN:2379-190X
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Shrnutí:Polar codes have drawn much attention and been adopted in 5G New Radio (NR) due to their capacity-achieving performance. Recently, as the emerging deep learning (DL) technique has breakthrough achievements in many fields, neural network decoder was proposed to obtain faster convergence and better performance than belief propagation (BP) decoding. However, neural networks are memory-intensive and hinder the deployment of DL in communication systems. In this work, a low-complexity recurrent neural network (RNN) polar decoder with codebook-based weight quantization is proposed. Our test results show that we can effectively reduce the memory overhead by 98% and alleviate computational complexity with slight performance loss.
ISSN:2379-190X
DOI:10.1109/ICASSP.2019.8683778