End-to-End Autoencoder for Drill String Acoustic Communications
This addresses a domain-specific problem for drilling efficiency and safety, but it is incremental as it applies an existing method (autoencoder) to a new application area.
The paper tackles the challenge of designing a low-latency, high-throughput, and reliable communication system for drill strings by proposing an end-to-end deep learning autoencoder, which outperforms a baseline non-contiguous OFDM system in terms of BER and PAPR with lower latency.
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.