Researching | Optimization of routing and wavelength optimization algorithm for optical transport network based on reinforcement learning

Infrared and Laser Engineering, Volume. 51, Issue 11, 20220084(2022)

Optimization of routing and wavelength optimization algorithm for optical transport network based on reinforcement learning

Yinghui Kong^1,2, Jiazhi Yang^1、*, Huisheng Gao^1,2, and Zhengwei Hu^1,2

Author Affiliations

¹Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, China

²Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding 071003, China

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Fig. 1. Optical transport network model

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Fig. 2. Structure of DeepRWA

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Fig. 3. Example of state

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Fig. 4. 14-node ¬NSFNET topology

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Fig. 5. Curve of blocking rate

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Fig. 6. Curve of resource occupancy

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Fig. 7. Curve of resource utilization rate vs. channel wavelength

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Fig. 8. Curve of policy entropy

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Fig. 9. Curve of value Loss

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Fig. 10. Algorithm’s execution time and converage speed

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Table 1. Serve parameters of simulation experiment
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Table 1. Serve parameters of simulation experiment
Parameters Value
Average arrival time of dynamic service/s 1/12
Continuing times of dynamic service/s 13
Available wavelength of channel 18

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Yinghui Kong, Jiazhi Yang, Huisheng Gao, Zhengwei Hu. Optimization of routing and wavelength optimization algorithm for optical transport network based on reinforcement learning[J]. Infrared and Laser Engineering, 2022, 51(11): 20220084

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Paper Information

Category: Optical communication and sensing

Received: Feb. 7, 2022

Accepted: --

Published Online: Feb. 9, 2023

The Author Email: Yang Jiazhi (yjz2458608317@126.com)

DOI:10.3788/IRLA20220084

Topics

laser devices and laser physics

Lasers and Laser Optics

laser manufacturing

Instrumentation, Measurement and Metrology