End-to-End Autoencoder for Drill String Acoustic Communications
<|reference_start|>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 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.<|reference_end|>
@article{lezhenin2024end-to-end,
    title={End-to-End Autoencoder for Drill String Acoustic Communications},
    author={Iurii Lezhenin, Aleksandr Sidnev, Vladimir Tsygan and Igor Malyshev},
    journal={arXiv preprint arXiv:2405.03840},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03840},
    primaryClass={cs.LG eess.SP}
}