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.
| Original language | English |
|---|---|
| Article number | 9143143 |
| Pages (from-to) | 1970-1974 |
| Number of pages | 5 |
| Journal | IEEE Wireless Communications Letters |
| Volume | 9 |
| Issue number | 11 |
| Early online date | 17 Jul 2020 |
| DOIs | |
| Publication status | Published - Nov 2020 |
| Externally published | Yes |
Bibliographical note
This work was supported in part by National Natural Science Foundation of China under Grant 61801082, and in part by Marie Skłodowska-Curie Individual Fellowship under Grant 793345. The work of Derrick Wing Kwan Ng was supported by the UNSW Digital Grid Futures Institute, UNSW, Sydney, under a cross-disciplinary fund scheme and by the Australian Research Council’s Discovery Project under Grant DP190101363.Keywords
- deep learning
- predictive beamforming
- UAV jittering