title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð´ÐµÐºÐ¾Ð´ÐµÑÐ° Ð¿Ð¾Ð¼ÐµÑ Ð¾ÑÑÑÐ¾Ð¹ÑÐ¸Ð²Ð¾Ð³Ð¾ ÐºÐ¾Ð´Ð° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
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: Development of a decoder of error-correcting code based on deep learning methods
Creators: name: ÐÑÐµÑÐ°ÑÐ½ÐµÐ², ÐÐ¸ÑÐ¸Ð»Ð»
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: 2022
Subjects: Ð´ÐµÐºÐ¾Ð´Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²ÑÐµ Ð´ÐµÐºÐ¾Ð´ÐµÑÑ
ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÐµ ÐºÐ¾Ð´Ñ
ofdm
ÑÐµÐºÑÑÑÐµÐ½ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
decoding
neural network decoder
convolutional code
neural network

description: ÐÐ±ÑÐµÐºÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ âÂ  Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²Ð¾Ð¹ Ð´ÐµÐºÐ¾Ð´ÐµÑ Ð¿Ð¾Ð¼ÐµÑÐ¾ÑÑÑÐ¾Ð¹ÑÐ¸Ð²Ð¾Ð³Ð¾ ÐºÐ¾Ð´Ð°. Ð¦ÐµÐ»Ñ ÑÐ°Ð±Ð¾ÑÑ â ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð´ÐµÐºÐ¾Ð´ÐµÑÐ° Ð¿Ð¾Ð¼ÐµÑÐ¾ÑÑÑÐ¾Ð¹ÑÐ¸Ð²Ð¾Ð³Ð¾ ÐºÐ¾Ð´Ð° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. ÐÐ»Ð¾Ðº Ð´ÐµÐºÐ¾Ð´Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾Ð¼ÐµÑÐ¾ÑÑÑÐ¾Ð¹ÑÐ¸Ð²Ð¾Ð³Ð¾ ÐºÐ¾Ð´Ð° ÑÐ²Ð»ÑÐµÑÑÑ Ð¾Ð´Ð½Ð¾Ð¹ Ð¸Ð· Ð²Ð°Ð¶Ð½ÑÑ ÑÐ°ÑÑÐµÐ¹ Ð»ÑÐ±Ð¾Ð³Ð¾ ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐ¸ÑÑÐ¾Ð²Ð¾Ð³Ð¾ Ð¿ÑÐ¸ÐµÐ¼Ð¾-Ð¿ÐµÑÐµÐ´Ð°ÑÑÐµÐ³Ð¾ ÑÑÑÑÐ¾Ð¹ÑÑÐ²Ð°. ÐÐ·-Ð·Ð° Ð±ÑÑÐ½Ð¾Ð³Ð¾ ÑÐ°Ð·Ð²Ð¸ÑÐ¸Ñ ÑÐ¸ÑÑÐ¾Ð²Ð¾Ð¹ ÑÐ²ÑÐ·Ð¸, Ð¸ Ð½ÐµÑÐ¼Ð¾ÑÑÑ Ð½Ð° Ð½Ð°Ð»Ð¸ÑÐ¸Ðµ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð´ÐµÐºÐ¾Ð´Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ, Ð²Ð¾Ð·Ð½Ð¸ÐºÐ°ÐµÑ Ð½ÐµÐ¾Ð±ÑÐ¾Ð´Ð¸Ð¼Ð¾ÑÑÑ Ð² ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ð¸ Ð½Ð¾Ð²ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð², ÐºÐ¾ÑÐ¾ÑÑÐµ Ð±Ñ ÑÑÐµÐ±Ð¾Ð²Ð°Ð»Ð¸ Ð¼ÐµÐ½ÑÑÐ¸Ð¹ Ð¾Ð±ÑÐµÐ¼ Ð¿Ð°Ð¼ÑÑÐ¸ Ð¸ Ð¼ÐµÐ½ÑÑÐ¸Ñ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ð¹. Ð Ð¾Ð´Ð½Ð¸Ð¼ Ð¸Ð· Ð¿ÐµÑÑÐ¿ÐµÐºÑÐ¸Ð²Ð½ÑÑ Ð½Ð°Ð¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ð¹ ÑÐ²Ð»ÑÐµÑÑÑ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ðµ Ð´ÐµÐºÐ¾Ð´ÐµÑÐ° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÑÐ¸ÑÑÐµÐ¼Ñ Ð¿ÑÐ¸ÐµÐ¼Ð°-Ð¿ÐµÑÐµÐ´Ð°ÑÐ¸ OFDM-ÑÐ¸Ð³Ð½Ð°Ð»Ð°, ÑÑÐ¸ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÐ¸, Ð½Ð° Ð´Ð°Ð½Ð½ÑÐ¹ Ð¼Ð¾Ð¼ÐµÐ½Ñ, Ð¿ÑÐ°ÐºÑÐ¸ÑÐµÑÐºÐ¸ Ð½Ðµ Ð¾ÑÐ²ÐµÑÐµÐ½Ñ Ð² Ð»Ð¸ÑÐµÑÐ°ÑÑÑÐµ. Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð±ÑÐ» ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²Ð¾Ð¹ Ð´ÐµÐºÐ¾Ð´ÐµÑ Ð´Ð»Ñ ÑÐ²ÑÑÑÐ¾ÑÐ½Ð¾Ð³Ð¾ ÐºÐ¾Ð´Ð° Ð½Ð° ÑÑÑÑ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ°Ñ: RNN (GRU, LSTM) Ð¸ CNN. ÐÑÐ»Ð¾ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð½Ð¾, ÑÑÐ¾ Ð´Ð»Ñ ÑÐµÐºÑÑÑÐµÐ½ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ Ð´Ð¾ÑÑÐ¸Ð³Ð°ÐµÑÑÑ Ð»ÑÑÑÐ°Ñ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÑ, Ð¸ Ð´Ð»Ñ ÑÐ»ÑÑÐ°Ð¹Ð½Ð¾Ð³Ð¾ Ð½Ð°Ð±Ð¾ÑÐ° Ð±Ð¸Ñ ÐºÑÐ¸Ð²ÑÐµ Ð¿Ð¾Ð¼ÐµÑÐ¾ÑÑÑÐ¾Ð¹ÑÐ¸Ð²Ð¾ÑÑÐ¸ ÑÑÑÐµÐ¼ÑÑÑÑ Ðº ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ°Ð¼ ÐºÐ»Ð°ÑÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð´ÐµÐºÐ¾Ð´Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÐÐ¸ÑÐµÑÐ±Ð¸ Ñ Ð¼ÑÐ³ÐºÐ¸Ð¼Ð¸ ÑÐµÑÐµÐ½Ð¸ÑÐ¼Ð¸. Ð¢Ð°ÐºÐ¶Ðµ Ð±ÑÐ»Ð° Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð° Ð¸Ð¼Ð¸ÑÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¿ÑÐ¸ÐµÐ¼Ð¾-Ð¿ÐµÑÐµÐ´Ð°ÑÑÐµÐ³Ð¾ ÑÑÑÑÐ¾Ð¹ÑÑÐ²Ð° Ð´Ð»Ñ OFDM-ÑÐ¸Ð³Ð½Ð°Ð»Ð°, Ð² Ð±Ð»Ð¾ÐºÐµ Ð´ÐµÐºÐ¾Ð´Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÐºÐ¾ÑÐ¾ÑÐ¾Ð¹ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð»Ð°ÑÑ ÑÐ°Ð±Ð¾ÑÐ° Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²Ð¾Ð³Ð¾ Ð´ÐµÐºÐ¾Ð´ÐµÑÐ°. ÐÑÐ»Ð¾ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¾, ÑÑÐ¾ Ð¸ Ð² ÑÑÐ¾Ð¼ ÑÐ»ÑÑÐ°Ðµ Ð¿ÑÐµÐ´Ð¿Ð¾ÑÑÐ¸ÑÐµÐ»ÑÐ½Ñ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²ÑÐµ Ð´ÐµÐºÐ¾Ð´ÐµÑÑ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ RNN, Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÑ ÐºÐ¾ÑÐ¾ÑÑÑ Ð¿ÑÐµÐ²Ð¾ÑÑÐ¾Ð´Ð¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ ÐÐ¸ÑÐµÑÐ±Ð¸ Ñ Ð¶ÐµÑÑÐºÐ¸Ð¼Ð¸ ÑÐµÑÐµÐ½Ð¸ÑÐ¼Ð¸ Ð¸ ÑÑÑÐµÐ¼Ð¸ÑÑÑ Ðº Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸ Ð¼ÑÐ³ÐºÐ¸Ñ ÑÐµÑÐµÐ½Ð¸Ð¹.
description: The object of research is a neural network decoder of an error-correcting code. The purpose of the work is to develop a decoder for an error-correcting code based on deep learning methods. The decoding block of the noise-correcting code is one of the important parts of any modern digital transceiver. Due to the rapid development of digital communication, and despite the existence of existing decoding algorithms, there is a need to create new methods that would require less memory and less calculations. And one of the promising directions is the creation of a decoder based on deep learning for an OFDM signal reception and transmission system, whose capabilities, at the moment, are practically not covered in the literature. In this work, a neural network decoder for a convolutional code was developed on three different modern architectures: RNN (GRU, LSTM) and CNN. It has been shown that the best performance is achieved for a recurrent neural architecture, and for a random set of bits, the noise immunity curves tend to the results of the classical soft-decision Viterbi decoding algorithm. A simulation model of a transceiver for an OFDM signal was also built, in the decoding unit of which the operation of a neural network decoder was studied. It was determined that in this case too, neural network decoders based on RNNs are preferred, the performance of which is superior to the Viterbi algorithm with hard decisions and tends to the performance of soft decisions.