Summary of End-to-end Autoencoder For Drill String Acoustic Communications, by Iurii Lezhenin et al.
End-to-End Autoencoder for Drill String Acoustic Communications
by Iurii Lezhenin, Aleksandr Sidnev, Vladimir Tsygan, Igor Malyshev
First submitted to arxiv on: 6 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an end-to-end deep learning autoencoder (AE) based communication system for acoustic drill string communications, aiming to achieve low latency, high throughput, and reliability. This system consists of transmitter and receiver implemented as feed-forward neural networks. The AE system outperforms a baseline non-contiguous OFDM system in terms of bit error rate (BER) and peak-to-average power ratio (PAPR), operating with lower latency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new communication system for drill strings that uses artificial intelligence to improve speed and reliability. The system is designed to be fast, efficient, and safe, which is important for drilling operations. The authors test their system in simulations and find that it performs better than other approaches. |
Keywords
» Artificial intelligence » Autoencoder » Deep learning
