End-to-End Autoencoder for Drill String Acoustic Communications

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 (AE) based end-to-end communication system, where tran...

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Bibliographic Details
Published inarXiv.org
Main Authors Lezhenin, Iurii, Sidnev, Aleksandr, Tsygan, Vladimir, Malyshev, Igor
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 06.05.2024
Subjects
Communications systems
Drilling
Drillstrings
Machine learning
Neural networks
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Summary: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 (AE) based end-to-end communication system, where transmitter and receiver implemented as feed forward neural networks, is proposed for acousticdrill string communications. Simulation shows that the AE system is able to outperform a baseline non-contiguous OFDM system in terms of BER and PAPR, operating with lower latency.
ISSN:2331-8422