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End-to-End Autoencoder for Drill String Acoustic Communications
May 8, 2024, 4:41 a.m. | Iurii Lezhenin, Aleksandr Sidnev, Vladimir Tsygan, Igor Malyshev
cs.LG updates on arXiv.org arxiv.org
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 …
abstract arxiv autoencoder challenge communication communications cs.lg deep learning design efficiency latency low low latency paper reliability safety string type
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