Advanced Photonics Nexus, Volume. 3, Issue 2, 026009(2024)
Multiparameter performance monitoring of pulse amplitude modulation channels using convolutional neural networks
Figures & Tables(19)
Fig. 1. Conceptual diagram of multiparameter performance monitoring of PAM signals in intra- and inter-data center systems. DAC, digital-to-analog converter; IM, intensity modulator; PD, photodiode; ADC, analog-to-digital converter; TBPF, tunable bandpass filter; SDN, software-defined networking; ROF, roll-off factor; OSNR, optical signal-to-noise ratio; CD, chromatic dispersion.
Fig. 2. (a) Experimental setup used to collect eye diagrams. ASE, amplified spontaneous emission; TBPF, tunable bandpass filter; EDFA, erbium-doped fiber amplifier; VOA, variable optical attenuator; DSP, digital signal processing; DAC, digital-to-analog converter; TDCM, tunable dispersion compensation module; PD, photodiode; OSA, optical spectrum analyzer; DCA, digital communication analyzer. (b) The structure of the VGG-based CNN model for classification. Conv, convolutional; BN, batch normalization; MP, max pooling; FC, fully connected.
Fig. 3. Eye diagrams of PAM signals with different MFs, BRs, PS, ROFs, OSNR, and CD.
Fig. 4. Features and parameters used in traditional ML methods (KNN, SVM, DT, and GBDT).
Fig. 5. Typical algorithm architectures applied in the VGG-based model, ResNet-18, MobileNetV3, and EfficientNetV2. PW, point-wise; DW, depth-wise.
Fig. 6. Confusion matrices of DT and GBDT for OSNR, CD, ROF, and BR classification tasks.
Fig. 7. Accuracy of joint monitoring parameters with different ML methods for (a) digital signal parameters and (b) optical link parameters. (c) Accuracy for all the 432 classes for each MF with different five-parameter combinations.
Fig. 8. Accuracy for all 1728 classes with different six-parameter combinations using DT, GBDT, KNN, SVM, and VGG-based CNN.
Fig. 9. (a) Accuracy curves and (b) distributions of VGG-based model, ResNet-18, MobileNetV3-S, and EfficientNetV2-S.
Fig. 11. Accuracy of different monitoring tasks using MTL and VGG-based CNN.
Table 1. Parameters used in hog and color histograms.
View tableView in ArticleTable 1. Parameters used in hog and color histograms.
Method Input image size HOG Color histogram Feature length Orientation Pixels per cell Cells per block Bin Range Channel KNN, SVM 320 × 320 × 3 9 16 × 16 2 × 2 256 0 to 255 3 (RGB) 13,764 DT, GBDT 320 × 320 × 3 9 64 × 64 2 × 2 256 0 to 255 3 (RGB) 1344
Table 2. Structure of the VGG-based model.
View tableView in ArticleTable 2. Structure of the VGG-based model.
Input size Filter size Layer Output size 320 × 320 × 3 3 × 3 × 3 × 30 Conv.1 320 × 320 × 30 320 × 320 × 30 2 × 2 × 30 MP.1 160 × 160 × 30 160 × 160 × 30 3 × 3 × 30 × 60 Conv.2 160 × 160 × 60 160 × 160 × 60 2 × 2 × 30 MP.2 80 × 80 × 60 80 × 80 × 60 3 × 3 × 60 × 80 Conv.3 80 × 80 × 80 80 × 80 × 80 2 × 2 × 80 MP.3 40 × 40 × 80 40 × 40 × 80 3 × 3 × 80 × 120 Conv.4 40 × 40 × 120 40 × 40 × 120 2 × 2 × 120 MP.4 20 × 20 × 120 48,000 48,000 × 4096 FC.1 4096 4096 4096 × 4096 FC.2 4096 4096 FC.3
Table 3. Accuracy of single-parameter classifications of different ML methods.
View tableView in ArticleTable 3. Accuracy of single-parameter classifications of different ML methods.
Method OSNR (%) CD (%) ROF (%) BR (%) PS (%) MF (%) KNN 99.44 99.92 95.58 95.41 99.96 100 DT 92.03 92.48 78.88 61.71 99.54 95.81 SVM 99.32 99.78 97.02 97.31 99.92 100 GBDT 99.61 98.82 95.41 88.37 99.98 99.81 VGG-based CNN 99.01 100 98.7 99.21 100 100
Table 4. Parameters selected of traditional ML methods.
View tableView in ArticleTable 4. Parameters selected of traditional ML methods.
Method PAM3 PAM4 PAM6 PAM8 All classes KNN DT MD = 400 MD = 400 MD = 400 MD = 400 MD = 400 MF = 300 MF = 400 MF = 300 MF = 900 MF = 600 SVM GBDT LR = 0.1 LR = 0.1 LR = 0.1 LR = 0.1 LR = 0.1 MD = 6 MD = 6 MD = 6 MD = 7 MD = 7 iter = 350 iter = 400 iter = 370 iter = 420 iter = 500
Table 5. Accuracy of classifications of GBDT, VGG-based CNN, and VGG-based CNN + GBDT.
View tableView in ArticleTable 5. Accuracy of classifications of GBDT, VGG-based CNN, and VGG-based CNN + GBDT.
Method OSNR (%) CD (%) ROF (%) BR (%) PS (%) MF (%) OSNR and CD (%) ROF and PS and BR (%) All classes (%) CNN 99.01 100 98.7 99.21 100 100 99.32 99.12 97.61 CNN + GBDT 99.16 99.61 98.81 98.1 100 100 99.53 98.11 83.13 GBDT 99.61 98.82 95.41 88.37 99.98 99.81 99.03 91.47 56.69
Table 6. Computational cost per image of the modern CNN models.
View tableView in ArticleTable 6. Computational cost per image of the modern CNN models.
Model name Input size FLOP Parameter Memory MobileNetV3-S 224×224×3 64.36 M 3.28 M 18.44 MB VGG-based 224×224×3 573.13 M 120 M 15.00 MB Resnet-18 224×224×3 1.82 G 12.06 M 28.53 MB EfficientNetV2-S 224×224×3 2.9 G 23.41 M 139.00 MB
Table 7. Structure of MobileNetV3-Small.
View tableView in ArticleTable 7. Structure of MobileNetV3-Small.
Input Operator Out channel SE NL Stride 320×320×3 Conv 3×3 16 — HS 2 160×160×16 MBConv 3×3 16 Yes RE 2 80×80×16 MBConv 3×3 24 — RE 2 40×40×24 MBConv 3×3 24 — RE 1 40×40×24 MBConv 5×5 40 Yes HS 2 20×20×40 MBConv 5×5 40 Yes HS 1 20×20×40 MBConv 5×5 40 Yes HS 1 20×20×40 MBConv 5×5 48 Yes HS 1 20×20×48 MBConv 5×5 48 Yes HS 1 20×20×48 MBConv 5×5 96 Yes HS 2 10×10×96 MBConv 5×5 96 Yes HS 1 10×10×96 MBConv 5×5 96 Yes HS 1 10×10×96 Conv 1×1 576 Yes HS 1 10×10×576 Pooling 7×7 576 — — 1 1×1×576 Conv 1×1, NBN 1280 — HS 1
Table 8. Weight of the tasks in the loss function.
View tableView in ArticleTable 8. Weight of the tasks in the loss function.
Task BR MF ROF PS OSNR CD Weight 1.01 0.81 0.99 0.78 0.98 0.79
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Si-Ao Li, Yuanpeng Liu, Yiwen Zhang, Wenqian Zhao, Tongying Shi, Xiao Han, Ivan B. Djordjevic, Changjing Bao, Zhongqi Pan, Yang Yue. Multiparameter performance monitoring of pulse amplitude modulation channels using convolutional neural networks[J]. Advanced Photonics Nexus, 2024, 3(2): 026009
Paper Information
Category: Research Articles
Received: Nov. 21, 2023
Accepted: Feb. 19, 2024
Published Online: Mar. 18, 2024
The Author Email: Yue Yang (yueyang@xjtu.edu.cn)
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