title: ÐÐ½Ð°Ð»Ð¸Ð· Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð´Ð»Ñ Ð´ÐµÐ¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ð¸ FTN-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: Analysis of the application of neural networks for the demodulation of FTN signals
Creators: name: ÐÐ±Ð´ÑÐ»Ð±Ð°ÑÐ¾Ð², ÐÐ·Ð°Ñ
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: faster-than-nyquist
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð¼ÐµÐ¶ÑÐ¸Ð¼Ð²Ð¾Ð»ÑÐ½Ð°Ñ Ð¸Ð½ÑÐµÑÑÐµÑÐµÐ½ÑÐ¸Ñ
Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ ÑÐ¸Ð³Ð½Ð°Ð»Ð°
ÑÐ¿ÐµÐºÑÑÐ°Ð»ÑÐ½Ð¾-ÑÑÑÐµÐºÑÐ¸Ð²Ð½ÑÐµ ÑÐ¸Ð³Ð½Ð°Ð»Ñ
ÑÐµÐ»ÐµÐºÐ¾Ð¼Ð¼ÑÐ½Ð¸ÐºÐ°ÑÐ¸Ð¸
machine learning
neural networks
intersymbol interference
signal detection
spectral effective signals
telecommunications

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¾Ð±Ð·Ð¾ÑÑ ÑÐ¿ÐµÐºÑÑÐ°Ð»ÑÐ½Ð¾-ÑÑÑÐµÐºÑÐ¸Ð²Ð½ÑÑ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Faster-than-Nyquist (FTN), Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼, Ð²Ð¾Ð·Ð½Ð¸ÐºÐ°ÑÑÐ¸Ñ Ð¿ÑÐ¸ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐµ Ð´Ð°Ð½Ð½ÑÑ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ð¸Ð·-Ð·Ð° Ð½ÐµÐ¾Ð±ÑÐ¾Ð´Ð¸Ð¼Ð¾Ð¹ Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½Ð¾Ð¹ ÑÐ»Ð¾Ð¶Ð½Ð¾ÑÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð²ÑÐ»ÐµÐ´ÑÑÐ²Ð¸Ðµ Ð²Ð²Ð¾Ð´Ð¸Ð¼Ð¾Ð¹ Ð¼ÐµÐ¶ÑÐ¸Ð¼Ð²Ð¾Ð»ÑÐ½Ð¾Ð¹ Ð¸Ð½ÑÐµÑÑÐµÑÐµÐ½ÑÐ¸Ð¸. Ð ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÑÑÑÑ Ð½Ð°ÑÑÐ½ÑÐµ Ð¿ÑÐ±Ð»Ð¸ÐºÐ°ÑÐ¸Ð¸ Ð¸ ÑÑÐ°ÑÑÐ¸, Ð½Ð°Ð¿ÑÐ°Ð²Ð»ÐµÐ½Ð½ÑÐµ Ð½Ð° ÑÐ½Ð¸Ð¶ÐµÐ½Ð¸Ðµ ÑÐ»Ð¾Ð¶Ð½Ð¾ÑÑÐ¸ Ð¿ÑÐ¸ÐµÐ¼Ð½Ð¸ÐºÐ¾Ð² Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð Ð°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÑÑÑÑ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð½ÑÐµ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ Ð²Ð½ÐµÐ´ÑÐµÐ½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð² ÑÑÑÑÐºÑÑÑÑ Ð¿ÑÐ¸ÐµÐ¼Ð½Ð¸ÐºÐ° Ð´Ð»Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ ÑÐµÐ»ÐµÐ¹ â Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð´ÐµÑÐµÐºÑÐ¾ÑÐ°, Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð´Ðµ-ÐºÐ¾Ð´ÐµÑÐ°, Ñ ÑÐµÐ»ÑÑ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ ÑÐ°Ð½ÐµÐµ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð½ÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¸ Ñ.Ð´. Ð¢Ð°Ðº Ð¶Ðµ, ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ñ Ð¸Ð´ÐµÐ¸ Ð²Ð½ÐµÐ´ÑÐµÐ½Ð¸Ñ FTN-ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ð² ÑÐ¸ÑÑÐµÐ¼Ñ ÑÐ²ÑÐ·Ð¸ Ð½Ð¾Ð²Ð¾Ð³Ð¾ Ð¿Ð¾ÐºÐ¾Ð»ÐµÐ½Ð¸Ñ, ÑÐ°ÐºÐ¸Ðµ ÐºÐ°Ðº Ð½ÐµÐ¾ÑÑÐ¾Ð³Ð¾Ð½Ð°Ð»ÑÐ½ÑÐ¹ Ð¼Ð½Ð¾Ð¶ÐµÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð´Ð¾ÑÑÑÐ¿ (Ð°Ð½Ð³Ð». NOMA), ÑÐ¿ÐµÐºÑÑÐ°Ð»ÑÐ½Ð¾-ÑÑÑÐµÐºÑÐ¸Ð²Ð½ÑÐµ ÑÐ¸Ð³Ð½Ð°Ð»Ñ Ñ ÑÐ°ÑÑÐ¾ÑÐ½ÑÐ¼ ÑÐ¿Ð»Ð¾ÑÐ½ÐµÐ½Ð¸ÐµÐ¼ (Ð°Ð½Ð³Ð». SEFDM).
description: The given work is devote to a review of spectrally efficient Faster-than-Nyquist (FTN) signals, problems arising in the processing of these signals due to the necessary computational complexity of classical algorithms due to the introduced intersymbol interference. The work considers scientific publications and articles aimed at reducing the complexity of receivers using machine-learning methods. The proposed architectures for introducing neural networks into the structure of the receiver for various purposes are considered - as a detector, as a decoder, in order to increase the efficiency of previously proposed algorithms, etc. Also, the ideas of introducing FTN signals into new generation communication systems, such as non-orthogonal multiple access (NOMA), spectrally efficient signals with frequency division multiplexing, are considered.