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arXiv · 2405.03840

End-to-End Autoencoder for Drill String Acoustic Communications

Iurii Lezhenin·Aleksandr Sidnev·Vladimir Tsygan·Igor Malyshev
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Abstract

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.

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Iurii Lezhenin, Aleksandr Sidnev, Vladimir Tsygan, Igor Malyshev. 2024-05-06. End-to-End Autoencoder for Drill String Acoustic Communications. https://arxiv.org/abs/2405.03840

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