UAV-to-ground path loss prediction based on synesthesia of machines

Mingran Sun; Lu Bai; Ziwei Huang; Xiang Cheng; Jianjun Wu

doi:10.1117/12.3032673

16 August 2024 UAV-to-ground path loss prediction based on synesthesia of machines

Mingran Sun, Lu Bai, Ziwei Huang, Xiang Cheng, Jianjun Wu

Proceedings Volume 13218, First Aerospace Frontiers Conference (AFC 2024); 1321825 (2024) https://doi.org/10.1117/12.3032673
Event: First Aerospace Frontiers Conference (AFC 2024), 2024, Xi’an, China

Abstract

In this paper, a real-time unmanned aerial vehicle (UAV)-to-ground path loss prediction model via intelligent multi-modal sensing-communication integration is developed in the operational mode of Synesthesia of Machine (SoM). In the modeling process, a dataset including multi-modal sensing and communication data is constructed in AirSim and Wireless InSite to support the exploration of the non-linear mapping relationship between physical environment and electromagnetic space. To explore the mapping relationship between the environmental features extracted from multi-modal sensing image data in physical environment and path loss in electromagnetic space, a convolution neural network (CNN) is constructed and trained. Therefore, based on the dataset, the real-time path loss prediction in the UAV-to-ground scenario is achieved. Simulation results show that the prediction average mean square error (MSE) of the proposed model is 6.4641 × 10^-5 in the test set. The accuracy and utility of the proposed model are validated by comparing the prediction results of the model and ray-tracing (RT)-based results.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingran Sun, Lu Bai, Ziwei Huang, Xiang Cheng, and Jianjun Wu "UAV-to-ground path loss prediction based on synesthesia of machines", Proc. SPIE 13218, First Aerospace Frontiers Conference (AFC 2024), 1321825 (16 August 2024); https://doi.org/10.1117/12.3032673

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