title: ÐÐµÑÐ¾Ð´Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð² ÑÐ¸ÑÑÐ¾Ð²Ð¾Ð¹ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐµ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð²
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Machine learning methods in in digital signal processing
Creators: name: Ð£ÑÐ¿ÐµÐ½ÑÐºÐ¸Ð¹, Ð¤ÐµÐ´Ð¾Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
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Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
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Ð¾Ð±ÑÐ°ÑÐ½Ð¾Ðµ ÑÐ°ÑÐ¿ÑÐ¾ÑÑÑÐ°Ð½ÐµÐ½Ð¸Ðµ
machine learning
DSP
nonlinear distortion
fiber-optic networks
distortion compensation
deep learning
neural network method
back propagation

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÐºÐ¾Ð¼Ð¿ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð½ÐµÐ»Ð¸Ð½ÐµÐ¹Ð½ÑÑ Ð¸ÑÐºÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð² Ð²Ð¾Ð»Ð¾ÐºÐ¾Ð½Ð½Ð¾-Ð¾Ð¿ÑÐ¸ÑÐµÑÐºÐ¸Ñ ÑÐµÑÑÑ ÑÐ²ÑÐ·Ð¸ Ñ Ð±Ð¾Ð»ÐµÐµ ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÐ¼ Ð¸ Ð°ÐºÑÑÐ°Ð»ÑÐ½ÑÐ¼ Ð¼ÐµÑÐ¾Ð´Ð¾Ð¼ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð³Ð»ÑÐ±Ð¾ÐºÐ¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. ÐÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÑÑ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ Ð¿Ð¾ ÑÑÐ¾Ð²Ð½Ñ Ð¾ÑÐ¸Ð±Ð¾Ðº Ð¸ ÑÐ»Ð¾Ð¶Ð½Ð¾ÑÑÐ¸ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸. Ð¢Ð°ÐºÐ¶Ðµ Ð¿ÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÑÑÑ ÑÐ¿Ð¾ÑÐ¾Ð±Ñ ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ Ð´Ð°Ð½Ð½Ð¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð°. ÐÐµÐ¹ÑÐ¾ÑÐµÑÑ Ð½Ð°Ð¿Ð¸ÑÐ°Ð½Ð° Ð½Ð° ÑÐ·ÑÐºÐµ Python Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐºÐ¸ Tensorflow Ð¸ ÐµÑ Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸Ð¹. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¾ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð¾, ÑÑÐ¾ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ 1-ÑÑÑÐ¿ÐµÐ½ÑÐ°ÑÐ¾Ð³Ð¾ Ð¾Ð±ÑÐ°ÑÐ½Ð¾Ð³Ð¾ ÑÐ°ÑÐ¿ÑÐ¾ÑÑÑÐ°Ð½ÐµÐ½Ð¸Ñ, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½Ð¾Ð³Ð¾ Ð½Ð° Ð³Ð»ÑÐ±Ð¾ÐºÐ¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÑÑ, Ð´Ð°ÐµÑ Ð²ÑÐ¸Ð³ÑÑÑ Ð¿Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ Ñ Ð¾Ð±ÑÑÐ½ÑÐ¼ 1 Ð¸ 50 â ÑÐ°Ð³Ð¾Ð²ÑÐ¼ DBP Ð½Ð° 1,6 Ð¸ 0,9 Ð´Ð ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²ÐµÐ½Ð½Ð¾. Ð ÑÐ²ÑÐ·Ð¸ Ñ ÑÑÐ¸Ð¼ Ð¼Ð¾Ð¶Ð½Ð¾ ÑÐ´ÐµÐ»Ð°ÑÑ Ð²ÑÐ²Ð¾Ð´, ÑÑÐ¾ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð³Ð»ÑÐ±Ð¾ÐºÐ¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð² Ð·Ð°Ð´Ð°ÑÐ°Ñ ÐºÐ¾Ð¼Ð¿ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð½ÐµÐ»Ð¸Ð½ÐµÐ¹Ð½ÑÑ Ð¸ÑÐºÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð² Ð²Ð¾Ð»Ð¾ÐºÐ¾Ð½Ð½ÑÑ ÑÐµÑÑÑ Ð¿ÑÐµÐ²Ð¾ÑÑÐ¾Ð´Ð¸Ñ Ð¿Ð¾ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð¸, Ð² ÑÐ²ÑÐ·Ð¸ Ñ ÑÐµÐ¼, ÑÐ²Ð»ÑÐµÑÑÑ Ð¿ÐµÑÑÐ¿ÐµÐºÑÐ¸Ð²Ð½ÑÐ¼ Ð½Ð°Ð¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸ÐµÐ¼.
description: This work is devoted to the comparison of classical methods of nonlinear distortion compensation in fiber-optic communication networks with a more modern and relevant method based on deep neural networks. The comparison is made by the level of errors and the complexity of the implementation. There is also suggest ways to improve this algorithm. The neural network is written in Python using the Tensorflow library and its applications. In the course of the work, it was found that the use of 1-step back propagation, based on deep neural networks, gives a gain compared to the usual 1 and 50 â step DBP of 1.6 and 0.9 dB, respectively. In this regard, it can be concluded that the use of deep neural networks in the problems of nonlinear distortion compensation in fiber networks is superior in efficiency and, therefore, is a promising direction.