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.
@article{arxiv.2405.03840,
title = {End-to-End Autoencoder for Drill String Acoustic Communications},
author = {Iurii Lezhenin and Aleksandr Sidnev and Vladimir Tsygan and Igor Malyshev},
journal= {arXiv preprint arXiv:2405.03840},
year = {2024}
}