K-Nearest Neighbors Gaussian Process Regression for Urban Radio Map Reconstruction

被引：14

作者：

Zhang, Yifang ^{[1
]}

Wang, Shaowei ^{[1
]}

机构：

[1] Nanjing Univ, Sch Elect Sci & Engn, Nanjing 210023, Peoples R China

来源：

IEEE COMMUNICATIONS LETTERS | 2022年 / 26卷 / 12期

基金：

中国国家自然科学基金;

关键词：

Gaussian process regression; k-nearest neighbors; radio map reconstruction; KERNEL;

D O I：

10.1109/LCOMM.2022.3207210

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

Radio map is of great importance to interference control, network planning and resource allocation in wireless communications. In this letter, we develop an accurate radio map reconstruction method based on k-nearest neighbors Gaussian process regression, which can exploit the relationship of the received signal strengths at adjacent locations efficiently. Numerical experiments demonstrate that our proposed method outperforms the state-of-the-art ones significantly.

引用

页码：3049 / 3053

页数：5

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