Traffic estimation based on long short-term memory neural network for mobile front-haul with XG-PON 
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1 Key Laboratory of Optical Fiber Sensing and Communications, Ministry of Education, University of Electronic Science and Technology of China, Chengdu 611731, China
2 Business School, University of International Business and Economics, Beijing 100029, China
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Fig. 1. MFH architecture based on the XG-PON system.
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Fig. 2. LSTM neural network architecture used in the proposed method.
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Fig. 3. LSTM cell and its unfolding in time.
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Fig. 4. Detailed structure of the LSTM cell memory block. The sigmoid function is denoted as and the pointwise multiplication is denoted as in the figure.
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Fig. 5. MSE in the training process.
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Fig. 6. Upstream delay performance comparison of RR-DBA, FNN-DBA, and LSTM-DBA.
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Fig. 7. Upstream jitter performance comparison of RR-DBA, FNN-DBA, and LSTM-DBA.
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Fig. 8. Upstream packet loss ratio performance comparison of RR-DBA, FNN-DBA, and LSTM-DBA.
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Fig. 9. Upstream delay performance comparison of RR-DBA, FNN-DBA, LSTM-DBA, and FBA for one active ONU case.
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Table1. Traffic Simulation Parameters
| Parameters | Values |
|---|
| Number of bursts | 5000 | | Mean burst time length | 2 ms | | Poisson arrival rate | [95, 110, 125–200 Mbps] | | Hurst parameter | 0.8 | | Pareto shape parameter | 1.4 | | Packet size | 1470 bytes |
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Table2. DBA Simulation Parameters
| Parameters | Values |
|---|
| Application traffic model | PPBP | | Simulation time | 10 s | | Max polling interval (all DBAs) | 125 μs | | Number of RRHs (ONUs) | 10 | | T-CONT per ONU | 1 | | Roundtrip propagation delay | 100 μs | | ONU queue size (T-CONT buffer) | 1 Mbytes |
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Min Zhang, Bo Xu, Xiaoyun Li, Yi Cai, Baojian Wu, Kun Qiu. Traffic estimation based on long short-term memory neural network for mobile front-haul with XG-PON[J]. Chinese Optics Letters, 2019, 17(7): 070603.
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