Инд. авторы:	Sidelnikov O.S., Skidin A.S., Sygletos S., Fedoruk M.P.
Заглавие:	Advanced methods to mitigate fiber nonlinearies using neural networks and probabilistic shaping
Библ. ссылка:	Sidelnikov O.S., Skidin A.S., Sygletos S., Fedoruk M.P. Advanced methods to mitigate fiber nonlinearies using neural networks and probabilistic shaping // Optics InfoBase Conference Papers: Advanced Photonics 2018 (BGPP, IPR, NP, NOMA, Sensors, Networks, SPPCom, SOF). - 2018: OSA - The Optical Society. - Art.JTu5A.48. - ISBN: 978-1-943580-43-9.
Внешние системы:	DOI: 10.1364/BGPPM.2018.JTu5A.48; РИНЦ: 35737566; SCOPUS: 2-s2.0-85051254266;
Реферат:	eng: We propose a combined approach to mitigate nonlinear fiber effects based on both the probabilistic shaping and static neural networks. We show that such combination can expand the system reach by 25-35%. © 2018 The Author(s).
Ключевые слова:	Photosensitivity; Static neural networks; Non-linear fiber; Light sensitive materials; Glass; Bragg gratings; Waveguides;
Издано:	2018
Конференция:	Название: Bragg Gratings, Photosensitivity and Poling in Glass Waveguides and Materials Аббревиатура: BGPPM 2018 Город: Zurich Страна: Switzerland Даты проведения: 2018-07-02 - 2018-07-05
Цитирование:	1. E. Giacoumidis, S. Mhatli, J. Wei, S. T. Le, I. Aldaya, M. F. Stephens, M. McCarthy, A. Ellis, N. J. Doran, and B. Eggleton, 'Intra and inter-channel nonlinearity compensation in WDM coherent optical OFDM using artificial neural network based nonlinear equalization,' in Optical Fiber Communication Conference (Optical Society of America, 2017), paper Th2A. 62 2. O. S. Sidelnikov, A. A. Redyuk, S. Sygletos, 'Dynamic neural network-based methods for compensation of nonlinear effects in multimode communication lines,' Quantum Electron. 47(12), 1147-1149 (2017) 3. A. S. Skidin, O. S. Sidelnikov, M. P. Fedoruk, and S. K. Turitsyn, 'Mitigation of nonlinear transmission effects for OFDM 16-QAM optical signal using adaptive modulation,' Opt. Express 24, 30296-30308 (2016) 4. M. Riedmiller, H. Braun, 'A direct adaptive method for faster backpropagation learning: the RPROP algorithm,' Proc ICNN, pp. 586-591, San Franscisco (1993)