Chinese Optics Letters, Volume. 21, Issue 3, 030602(2023)
Fiber communication receiver models based on the multi-head attention mechanism
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Fig. 1. Structure of the communication scenario, the traditional receiver, and the AI-based receiver model containing multi-head attention mechanism.
Fig. 3. Performance of convergence of AI-based receiver model through training.
Table 1. Important Parameters for the Model, Data Set, and Numerical Demonstration
View tableView in ArticleTable 1. Important Parameters for the Model, Data Set, and Numerical Demonstration
Parameter Value Parameter Value Modulation format OOK/PAM Symbol rate 10/20/40 GBaud Sampling rate Transmission distance 0–100 km Scale of training data set 31,744 symbols per distance SNR 0–16 dB and +inf for OOK; 0–20 dB and +inf for PAM Scale of validation data set 1600 symbols per distance Scale of test data set 523,264 symbols per distance Configurations for training set 1–100 km, interval of 1 km Configurations for test set 0.5–99.5 km, interval of 1 km Configurations for validation set 1–100 km, interval of 1 km Central wavelength 1550 nm Optimizer ADAM Batch size 4096 symbols Loss function NMSE Valuation function Bit error rate Power of laser source 0 dBm Modulator Intensity modulator Fiber in transmission link SSMF (G.652) Responsivity of PD 1 A/W Dispersion 16.75 ps·nm−1·km−1 Effective area
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Yubin Zang, Zhenming Yu, Kun Xu, Minghua Chen, Sigang Yang, Hongwei Chen, "Fiber communication receiver models based on the multi-head attention mechanism," Chin. Opt. Lett. 21, 030602 (2023)
Paper Information
Category: Fiber Optics and Optical Communications
Received: Aug. 14, 2022
Accepted: Oct. 17, 2022
Posted: Oct. 18, 2022
Published Online: Nov. 16, 2022
The Author Email: Zhenming Yu (yuzhenming@bupt.edu.cn), Hongwei Chen (chenhw@tsinghua.edu.cn)
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