CNN-Based Path Loss Prediction With Enhanced Satellite Images

被引:2
作者
Qiu, Zhicheng [1 ,2 ]
He, Ruisi [1 ,2 ]
Yang, Mi [1 ,2 ]
Zhou, Shun [3 ]
Yu, Long [3 ]
Wang, Chenlong [1 ,2 ]
Zhang, Yuxin [1 ,2 ]
Fan, Jianhua [3 ]
Ai, Bo [1 ,2 ]
机构
[1] Beijing Jiaotong Univ, Sch Elect & Informat Engn, State Key Lab Adv Rail Autonomous Operat, Beijing 100044, Peoples R China
[2] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Frontiers Sci Ctr Smart High Speed Railway Syst R, Beijing 100044, Peoples R China
[3] Natl Univ Def Technol, Res Inst 63, Nanjing 210007, Peoples R China
来源
IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS | 2024年 / 23卷 / 01期
基金
中国国家自然科学基金;
关键词
Satellite images; Predictive models; Buildings; Roads; Vegetation mapping; Receivers; Radio frequency; Deep learning (DL); environmental characterization; path loss (PL); radio propagation; satellite images; STORED ELECTROMAGNETIC ENERGY; IMPEDANCE;
D O I
10.1109/LAWP.2023.3321037
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Precise path loss models play a crucial role in design and optimization of wireless communication systems, requiring a careful balance between accuracy and efficiency. Deep learning presents a promising approach for improving both aspects. This letter proposes a novel approach that uses satellite images to construct a comprehensive dataset with rich environmental information. By incorporating environmental features into a convolutional-neural-network-based model, the accuracy of path loss prediction is significantly enhanced. Comparisons and validation demonstrate the approach in improving the accuracy of path loss prediction, particularly with the assistance of road information.
引用
收藏
页码:189 / 193
页数:5
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