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.
Ruichen Wang, Jingyang Lu, Yiran Xu, Dan Shen, Genshe Chen, Khanh Pham, and Erik Blasch, "Intelligent path loss prediction engine design using machine learning in the urban outdoor environment," Proc. SPIE 10641, Sensors and Systems for Space Applications XI, 106410J (Presented at SPIE Defense + Security: April 16, 2018; Published: 2 May 2018); https://doi.org/10.1117/12.2305204.
Conference Presentations are recordings of oral presentations given at SPIE conferences and published as part of the conference proceedings. They include the speaker's narration along with a video recording of the presentation slides and animations. Many conference presentations also include full-text papers. Search and browse our growing collection of more than 14,000 conference presentations, including many plenary and keynote presentations.