End-to-End Autoencoder for Drill String Acoustic Communications

TL;DR

This paper proposes a deep learning autoencoder-based end-to-end acoustic drill string communication system that improves throughput and reliability while reducing latency compared to traditional methods.

Contribution

It introduces a novel autoencoder architecture for drill string communications, demonstrating superior performance over baseline systems in simulations.

Findings

Outperforms non-contiguous OFDM in BER and PAPR

Operates with lower latency

Shows promise for efficient drill string communication

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 BER and PAPR, operating with lower latency.