  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.
