Computer vision assisted mmWave beamforming for UAV-to-vehicle links
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).
Cited Times [WOS]:0
|Document Type||Conference paper|
|Department||Department of Electrical and Electronic Engineering|
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.
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