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Intelligent path loss prediction engine design using machine learning in the urban outdoor environment
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Paper Abstract

Due to the progressive expansion of public mobile networks and the dramatic growth of the number of wireless users in recent years, researchers are motivated to study the radio propagation in urban environments and develop reliable and fast path loss prediction models. During last decades, different types of propagation models are developed for urban scenario path loss predictions such as the Hata model and the COST 231 model. In this paper, the path loss prediction model is thoroughly investigated using machine learning approaches. Different non-linear feature selection methods are deployed and investigated to reduce the computational complexity. The simulation results are provided to demonstratethe validity of the machine learning based path loss prediction engine, which can correctly determine the signal propagation in a wireless urban setting.

Paper Details

Date Published: 2 May 2018
PDF: 7 pages
Proc. SPIE 10641, Sensors and Systems for Space Applications XI, 106410J (2 May 2018); doi: 10.1117/12.2305204
Show Author Affiliations
Ruichen Wang, Intelligent Fusion Technology, Inc. (United States)
Jingyang Lu, Intelligent Fusion Technology, Inc. (United States)
Yiran Xu, Intelligent Fusion Technology, Inc. (United States)
Dan Shen, Intelligent Fusion Technology, Inc. (United States)
Genshe Chen, Intelligent Fusion Technology, Inc. (United States)
Khanh Pham, Air Force Research Lab. (United States)
Erik Blasch, Air Force Office of Scientific Research (United States)


Published in SPIE Proceedings Vol. 10641:
Sensors and Systems for Space Applications XI
Khanh D. Pham; Genshe Chen, Editor(s)

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