基于感知图像信息的无人机信道路径损耗预测

doi:10.16451/j.cnki.issn1003-6059.202311002

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Abstract To facilitate the application and development of 6G unmanned aerial vehicle(UAV)-to-ground wireless communications, improve the theory foundation of UAV-to-ground communication system and meet the safety and efficiency requirements of 6G communications, sensing image data based UAV channel path loss prediction in 6G UAV-to-ground communication scenario is studied. Firstly, based on AirSim and Wireless InSite, sensing data simulation platform and channel data simulation platform, a mixed sensing and communication integration dataset for a dynamic UAV-to-ground communication scenario, is established to explore the mapping relationship between physical space and electromagnetic space. Secondly, based on the established dataset, the mapping relationship between sensing image in physical space and channel path loss in electromagnetic space is built and the 6G UAV-to-ground real-time path loss prediction is achieved. Finally, the prediction result of the proposed model is compared with the test set through the simulation test and the results verify the accuracy of the proposed model.

Key words： Sensing and Communication Integration Path Loss Prediction Synesthesia Mechanism Unmanned Aerial Vehicle Communications

Received: 09 October 2023

ZTFLH:

TN92

Fund:National Natural Science Foundation of China(No.62125101,62341101,62371273,62001018,62106282), Natural Science Foundation of Shandong Province(No.ZR2023YQ058), New Cornerstone Science Foundation through the XPLORER PRIZE, Young Elite Scientists Sponsorship Program by CAST(No.2023QNRC001), Taishan Scholars Program, Beijing Science and Technology New Star Program(No.20220484139)

Corresponding Authors: BAI Lu, Ph.D., professor. Her research interests include 6G wireless communication network channel mea-surements and modeling.

About author:: SUN Mingran, Ph.D. candidate. His research interests include AI-based channel modeling. HUANG Ziwei, Ph.D. candidate. His research interests include complex high-mobility communication channel measurements and mo-deling. CHENG Xiang, Ph.D., professor. His research interests include data-driven intelligent network and networked intelligence. ZHANG Hongguang, Ph.D., assistant professor. His research interests include few-shot learning, meta-learning and transfer-lear-ning.FENG Tao, Ph.D., senior engineer. His research interests include intelligent network, software defined network and network management.

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	SUN Mingran
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	FENG Tao

Cite this article:

SUN Mingran,HUANG Ziwei,BAI Lu等. Sensing Image Data Based Unmanned Aerial Vehicle Channel Path Loss Prediction[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(11): 987-996.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202311002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2023/V36/I11/987

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