Research on the Macrocell Wireless Channel Model Based on Physic‐Inspired Support Vector Regression Algorithm Wireless Channel Model in Macrocell Environment

To support wireless network planning and optimization in macrocellular environments, this paper presents an efficient machine learning method capable of achieving rapid predictive simulations at the kilometer level. The method embeds the building transmission model (BTM) into a machine learning fram...

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Vydané v:International journal of antennas and propagation Ročník 2025; číslo 1
Hlavní autori: Yao, Qi, Liu, Zhongyu, Guo, Lixin, Guo, Jiang, Nan, Zuoyong, Liu, Wei, Li, Jiangting
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York John Wiley & Sons, Inc 01.01.2025
Wiley
Predmet:
Accuracy
Buildings
Efficiency
Hyperplanes
Machine learning
Methods
Neural networks
Optimization
Propagation
Regression models
Support vector machines
Wireless networks
ISSN:1687-5869, 1687-5877
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Popis
Shrnutí:To support wireless network planning and optimization in macrocellular environments, this paper presents an efficient machine learning method capable of achieving rapid predictive simulations at the kilometer level. The method embeds the building transmission model (BTM) into a machine learning framework based on support vector regression. The core of this new regression model lies in mapping the antenna and environmental feature information through the BTM into a high‐dimensional space and then seeking the optimal hyperplane in this space that maximizes the margin with respect to the training data. In addition, a model was constructed and validated based on measured wireless channel data at frequencies of 900 and 3500 MHz. The results show that the root mean square error between the proposed model and the measured results is less than 7 dB, and the single‐point simulation time of the proposed model for kilometer‐scale large‐scale coverage prediction is in the millisecond range.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1687-5869
1687-5877
DOI:10.1155/ijap/1126365