Iterative Soft Decoding of Reed-Solomon Codes Based on Deep Learning

In this letter, a deep learning based iterative soft decision decoding algorithm for Reed-Solomon codes is proposed. This algorithm takes advantage of deep neural network and Stochastic Shifting based Iterative Decoding (SSID). By assigning weights to every edge in the Tanner graph and changing the...

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Vydáno v:IEEE communications letters Ročník 24; číslo 9; s. 1991 - 1994
Hlavní autoři: Zhang, Wei, Zou, Shuming, Liu, Yanyan
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.09.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Artificial neural networks
Biological neural networks
Codes
Computer simulation
Decoding
Deep learning
Iterative decoding
Machine learning
Neural networks
Reed-Solomon codes
Soft decision decoding
stochastic shifting based iterative decoding (SSID)
Training
ISSN:1089-7798, 1558-2558
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Popis
Shrnutí:In this letter, a deep learning based iterative soft decision decoding algorithm for Reed-Solomon codes is proposed. This algorithm takes advantage of deep neural network and Stochastic Shifting based Iterative Decoding (SSID). By assigning weights to every edge in the Tanner graph and changing the architecture of SSID method, the proposed neural network decoder achieves better decoding performance. Compared with BM decoding, HDD-LCC and traditional SSID, simulation results show that for RS (15, 11), this algorithm provides coding gain up to 1.5dB, 1.1dB and 0.5dB, respectively when FER = 10 −2 .
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content type line 14
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2020.2992488