A neural network model of erbium-doped photonic crystal fibre amplifiers |
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| Authors: | G. Fornarelli L. Mescia F. Prudenzano M. De Sario F. Vacca |
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| Affiliation: | 1. Laboratoire Interdisciplinaire Carnot de Bourgogne, UMR 6303 CNRS-Université de Bourgogne, 9 avenue Alain Savary, BP 47870, 21078 Dijon Cedex, France;2. FEMTO-ST/Optics department, UMR 6174 CNRS-University of Franche-Comté, 16 route de Gray, 25030 Besançon Cedex, France;3. Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Brescia, via Branze 38, 25123 Brescia, Italy;1. Insitute of Laser, School of Science, Beijing Jiaotong University, Beijing 100044, China;2. State Key Laboratory of Metastable Materials Science and Technology, Yanshan University, Qinhuangdao 066004, China;1. Center for Applied Informatics and College of Enginering and Science, Victoria University, Footscray, VIC3011, Australia;2. School of Systems Engineering, University of Reading, Reading RG6 6AY, UK;1. State Key Laboratory of Information Photonics and Optical Communications, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China;2. China Internet Network Information Center, Beijing 100190, China |
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| Abstract: | The evaluation of the general evolution equations that describe the longitudinal propagation of pump, signal, forward and backward amplified spontaneous emission in rare-earth-doped optical fibre amplifier could be computationally expensive. In this paper, to reduce the computational time, a neural network approach for the modeling of erbium-doped photonic crystal fibre amplifiers is proposed. A number of simulations have been performed to investigate the characteristics of the proposed approach. The numerical results show good agreement between the neural network approach and the conventional algorithm based on the solution of the power evolution equations. The proposed approach exhibits attractive performance in terms of flexibility, accuracy and computational time. |
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