Alanezi, Mohammed A;
Mohammad, Abdullahi;
Sha’aban, Yusuf A;
Bouchekara, Houssem REH;
Shahriar, Mohammad S;
(2022)
Auto-Encoder Learning-Based UAV Communications for Livestock Management.
Drones
, 6
(10)
, Article 276. 10.3390/drones6100276.
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Abstract
The advancement in computing and telecommunication has broadened the applications of drones beyond military surveillance to other fields, such as agriculture. Livestock farming using unmanned aerial vehicle (UAV) systems requires surveillance and monitoring of animals on relatively large farmland. A reliable communication system between UAVs and the ground control station (GCS) is necessary to achieve this. This paper describes learning-based communication strategies and techniques that enable interaction and data exchange between UAVs and a GCS. We propose a deep auto-encoder UAV design framework for end-to-end communications. Simulation results show that the auto-encoder learns joint transmitter (UAV) and receiver (GCS) mapping functions for various communication strategies, such as QPSK, 8PSK, 16PSK and 16QAM, without prior knowledge.
| Type: | Article |
|---|---|
| Title: | Auto-Encoder Learning-Based UAV Communications for Livestock Management |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/drones6100276 |
| Publisher version: | https://doi.org/10.3390/drones6100276 |
| Language: | English |
| Additional information: | This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Unmanned aerial vehicle; convolutional auto-encoder; livestock farming; deep neural networks |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10157130 |
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