End-to-End Autoencoder for Drill String Acoustic Communications

arXiv:2405.03840v1 Announce Type: new
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