title: ÐÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²Ð¾Ð³Ð¾ Ð°Ð²ÑÐ¾ÐºÐ¾Ð´Ð¸ÑÐ¾Ð²ÑÐ¸ÐºÐ° Ð´Ð»Ñ Ð¿ÐµÑÐµÐ´Ð°ÑÐ¸ Ð´Ð°Ð½Ð½ÑÑ Ð¿Ð¾ Ð±ÑÑÐ¸Ð»ÑÐ½Ð¾Ð¹ ÐºÐ¾Ð»Ð¾Ð½Ð½Ðµ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Using a neural network autoencoder for drill string communications
Creators: name: ÐÐµÐ¶ÐµÐ½Ð¸Ð½, Ð®ÑÐ¸Ð¹
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: ÐÑÑÐµÐ½Ð¸Ðµ ÐºÐ¾Ð»Ð¾Ð½ÐºÐ¾Ð²Ð¾Ðµ
ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
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description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â«ÐÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²Ð¾Ð³Ð¾ Ð°Ð²ÑÐ¾ÐºÐ¾Ð´Ð¸ÑÐ¾Ð²ÑÐ¸ÐºÐ° Ð´Ð»Ñ Ð¿ÐµÑÐµÐ´Ð°ÑÐ¸ Ð´Ð°Ð½Ð½ÑÑ Ð¿Ð¾ Ð±ÑÑÐ¸Ð»ÑÐ½Ð¾Ð¹ ÐºÐ¾Ð»Ð¾Ð½Ð½ÐµÂ». ÐÐµÑÐµÐ´Ð°ÑÐ° Ð´Ð°Ð½Ð½ÑÑ Ð¿Ð¾ Ð±ÑÑÐ¸Ð»ÑÐ½Ð¾Ð¹ ÐºÐ¾Ð»Ð¾Ð½Ð½Ðµ ÑÐ²Ð»ÑÐµÑÑÑ Ð²Ð°Ð¶Ð½Ð¾Ð¹ Ð·Ð°Ð´Ð°ÑÐµÐ¹ Ð² ÐºÐ¾Ð½ÑÐµÐºÑÑÐµ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð¸ Ð±ÐµÐ·Ð¾Ð¿Ð°ÑÐ½Ð¾ÑÑÐ¸ Ð±ÑÑÐµÐ½Ð¸Ñ. Ð Ð½Ð°ÑÑÐ¾ÑÑÐµÐµ Ð²ÑÐµÐ¼Ñ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° ÑÐ¸ÑÑÐµÐ¼Ñ Ð¿ÐµÑÐµÐ´Ð°ÑÐ¸ Ð´Ð°Ð½Ð½ÑÑ Ð¿Ð¾ Ð±ÑÑÐ¸Ð»ÑÐ½Ð¾Ð¹ ÐºÐ¾Ð»Ð¾Ð½Ð½Ðµ, ÐºÐ¾ÑÐ¾ÑÐ°Ñ Ð¸Ð¼ÐµÐ»Ð° Ð±Ñ Ð½Ð¸Ð·ÐºÑÑ Ð·Ð°Ð´ÐµÑÐ¶ÐºÑ, Ð½Ð¾ ÑÐ´Ð¾Ð²Ð»ÐµÑÐ²Ð¾ÑÑÐ»Ð° ÑÑÐµÐ±Ð¾Ð²Ð°Ð½Ð¸ÑÐ¼ Ð¾ÑÐ½Ð¾ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾ Ð½Ð°Ð´ÐµÐ¶Ð½Ð¾ÑÑÐ¸ Ð¸ Ð¿ÑÐ¾Ð¿ÑÑÐºÐ½Ð¾Ð¹ ÑÐ¿Ð¾ÑÐ¾Ð±Ð½Ð¾ÑÑÐ¸, ÑÐ²Ð»ÑÐµÑÑÑ Ð°ÐºÑÑÐ°Ð»ÑÐ½Ð¾Ð¹ Ð·Ð°Ð´Ð°ÑÐµÐ¹. Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÐµÑÑÑ Ð¿ÐµÑÑÐ¿ÐµÐºÑÐ¸Ð²Ð° Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ ÑÐ¸ÑÑÐµÐ¼Ñ ÑÐ²ÑÐ·Ð¸ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²Ð¾Ð³Ð¾ Ð°Ð²ÑÐ¾ÐºÐ¾Ð´Ð¸ÑÐ¾Ð²ÑÐ¸ÐºÐ° Ð´Ð»Ñ Ð°ÐºÑÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð¿ÐµÑÐµÐ´Ð°ÑÐ¸ Ð´Ð°Ð½Ð½ÑÑ Ð¿Ð¾ Ð±ÑÑÐ¸Ð»ÑÐ½Ð¾Ð¹ ÐºÐ¾Ð»Ð¾Ð½Ð½Ðµ. ÐÑÐ»Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð° ÑÐ¸ÑÑÐµÐ¼Ð° ÑÐ²ÑÐ·Ð¸ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð°Ð²ÑÐ¾ÐºÐ¾Ð´Ð¸ÑÐ¾Ð²ÑÐ¸ÐºÐ° Ð¸ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¾ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÐµÐµ ÑÐ°Ð±Ð¾ÑÑ. ÐÐ¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ Ñ Ð±Ð°Ð·Ð¾Ð²Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ð¾Ð¹ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ OFDM Ñ Ð½ÐµÑÐ¼ÐµÐ¶Ð½ÑÐ¼Ð¸ Ð½ÐµÑÑÑÐ¸Ð¼Ð¸, Ð°Ð²ÑÐ¾ÐºÐ¾Ð´Ð¸ÑÐ¾Ð²ÑÐ¸Ðº Ð¾Ð±ÐµÑÐ¿ÐµÑÐ¸Ð²Ð°ÐµÑ Ð»ÑÑÑÑÑ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÑ Ñ ÑÐ¾ÑÐºÐ¸ Ð·ÑÐµÐ½Ð¸Ñ BER Ð¸ PAPR, ÑÐ°Ð±Ð¾ÑÐ°Ñ Ñ ÑÐ¾Ð¹ Ð¶Ðµ Ð¿ÑÐ¾Ð¿ÑÑÐºÐ½Ð¾Ð¹ ÑÐ¿Ð¾ÑÐ¾Ð±Ð½Ð¾ÑÑÑÑ, Ð½Ð¾ Ð¼ÐµÐ½ÑÑÐµÐ¹ Ð·Ð°Ð´ÐµÑÐ¶ÐºÐ¾Ð¹.
description: The subject of the graduate qualification work is Â«Using a neural network autoencoder for drill string communicationsÂ». Drill string communications are important for drilling efficiency and safety. The design of a low latency drill string communication system with high throughput and reliability remains an open challenge. In this paper a deep learning autoencoder based end-to-end communication system, where transmitter and receiver implemented as feed forward neural networks, is proposed for acoustic drill string communications. The autoencoder was implemented and compared with a baseline non-contiguous OFDM in terms of BER and PAPR. Simulation shows that the AE system is able to outperform a baseline non-contiguous OFDM having the same throughput, but operating with lower latency.