Yuan, W;
Liu, C;
Liu, F;
Li, S;
Ng, DWK;
(2020)
Learning-Based Predictive Beamforming for UAV Communications With Jittering.
IEEE Wireless Communications Letters
, 9
(11)
pp. 1970-1974.
10.1109/LWC.2020.3009951.
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Abstract
As a promising technique for realizing future wireless networks, unmanned aerial vehicle (UAV) communications have drawn numerous attentions. The performance of practical UAV communication systems is limited by the presence of inevitable jittering due to the inherent random wind gusts. The jittering introduces angle ambiguity which is challenging for aligning the information beams between the UAV-mounted base station (BS) and the user equipment (UE). This letter develops a learning-based predictive beamforming scheme to address the beam misalignment caused by UAV jittering. In particular, a deep learning approach is adopted to predict the angles between the UAV and the UE. By doing so, the UAV and the UE can prepare the transmit and receive beams in advance, which enables reliable UAV-based communication. Simulation results verify that the communication performance of the proposed scheme is robust to the presence of UAV jittering.
| Type: | Article |
|---|---|
| Title: | Learning-Based Predictive Beamforming for UAV Communications With Jittering |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/LWC.2020.3009951 |
| Publisher version: | https://doi.org/10.1109/LWC.2020.3009951 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | UAV jittering, deep learning, predictive beamforming |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS 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 |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10117127 |
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