A Calibrated Permittivity Modeling Approach for Cross-Area Path Loss Prediction

Accurate path loss prediction (PLP) in target areas (TAs) is required in many wireless communication applications. However, in real-world deployment, there only may be available path loss data outside TAs. In this letter, a framework based on calibrating the modeling of buildings' permittivity...

Full description

Saved in:
Bibliographic Details
Published in:IEEE wireless communications letters 2023-08, Vol.12 (8), p.1299-1303
Main Authors: Jiang, Yuanhao, Zhou, Shidong, Zhong, Xiaofeng
Format: Article
Language:English
Subjects:
Algorithms
Buildings
calibrated permittivity modeling
Calibration
Data models
Loss measurement
Message passing
Modelling
Optimization
Path loss prediction
Permittivity
Permittivity measurement
Predictive models
radio propagation
Ray tracing
Reference signals
Wavelength measurement
Wireless communications
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Accurate path loss prediction (PLP) in target areas (TAs) is required in many wireless communication applications. However, in real-world deployment, there only may be available path loss data outside TAs. In this letter, a framework based on calibrating the modeling of buildings' permittivity for cross-area PLP is proposed, with the advantage of not requiring data from TAs. The proposed framework requires only the Reference Signal Received Power (RSRP) data from other measured areas (MAs, no intersection with TAs), to learn more precise permittivity modeling and indirectly enhance the prediction in TAs. Furthermore, we exploit a message-passing based algorithm and its low-complexity implementation in the optimization of calibrating permittivity modeling, to improve the prediction accuracy and reduce the iterations required. Real-world measurement is performed and the results have revealed a better performance improvement of the proposed method, compared with Random Forest (RF) and ray-tracing (RT).
ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2022.3232380