Computer vision assisted mmWave beamforming for UAV-to-vehicle links

Zou，Jiaqi1,2,3; Cui，Yuanhao1,2,3; Zou，Zixuan1,2,3; Liu，Yuyang1,2,3; Zhang，Guanyu1,2,3; Sun，Songlin1,2,3; Yuan，Weijie4

10.1145/3556562.3558565

2022-10-21

ISACom 2022 - Proceedings of the 2022 1st ACM MobiCom Workshop on Integrated Sensing and Communications Systems, Part of MobiCom 2022

7-11

This paper focuses on the beamforming algorithm for UAV-to-vehicle communications. To deal with high communication overhead caused by beam tracking in high mobility communication scenarios, we utilize the inherent vision functionality of UAV platforms and propose a vision-assisted beamforming framework. We propose to use a deep-learning-based network for vehicle detection. Based on the predicted positions of vehicles, we propose a lightweight beamforming algorithm to save beam tracking overhead. Experiments and simulations are implemented on the UAV detection and tracking (UAVDT) dataset, which shows that the proposed algorithm gains a significant performance on received signal-to-interference-plus-noise ratio (SINR).

Integrated sensing and communications predictive beamforming unmanned aerial vehicle

Others

English

2-s2.0-85140208714

Scopus

1.Beijing University of Posts and Telecommunications (BUPT),Beijing,China
2.National Engineering Laboratory for Mobile Network Security,Bupt,Beijing,China
3.Key Laboratory of Trustworthy Distributed Computing and Service (BUPT),Ministry of Education,China
4.Department of Electrical and Electronic Engineering,Southern University of Science and Technology,Shenzhen,China

Zou，Jiaqi,Cui，Yuanhao,Zou，Zixuan,et al. Computer vision assisted mmWave beamforming for UAV-to-vehicle links[C],2022:7-11.