Probabilistic shaping communication system aided by neural network distribution matcher in data center optical network

A neural network (NN)‐assisted probabilistic shaping (PS) distribution matcher is proposed, in which the model is simplified by a structured optimization method. The NN algorithm can encode the information sequence, making the signal obey the Gaussian distribution, and can directly restore the recei...

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Bibliographic Details
Published in:Microwave and optical technology letters 2021-09, Vol.63 (9), p.2274-2278
Main Authors: Jing, Zexuan, Tian, Qinghua, Xin, Xiangjun, Wang, Yongjun, Guo, Dong, Sheng, Xia, Yu, Chao
Format: Article
Language:English
Subjects:
Algorithms
Communications systems
Data centers
Error correction
machine learning
Neural networks
Normal distribution
Optical communication
optical fiber communication
Optimization
probabilistic shaping
Statistical analysis
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Summary:A neural network (NN)‐assisted probabilistic shaping (PS) distribution matcher is proposed, in which the model is simplified by a structured optimization method. The NN algorithm can encode the information sequence, making the signal obey the Gaussian distribution, and can directly restore the received signal. In addition, the algorithm uses the novel training method at both ends of the transmitter and receiver so that the system performance is significantly improved. PS system verification experiments have been carried out under 16QAM‐DMT modulation format. Under the hard decision forward error correction (FEC) threshold of 3.8*10−3 BER, the proposed system achieves 1.1 dB improvement compared to the traditional 16QAM‐DMT system.
ISSN:0895-2477
1098-2760
DOI:10.1002/mop.32930