JCSE, vol. 15, no. 2, pp.84-95, June, 2021

DOI: http://dx.doi.org/10.5626/JCSE.2021.15.2.84

A Practical Approach to Indoor Path Loss Modeling Based on Deep Learning

Shengjie Ma, Hong Cheng, and Hyukjoon Lee
Department of Computer Engineering, Kwangwoon University, Seoul, Korea

Abstract: Deep learning has become one of the most powerful prediction approaches, and it can be used to solve classification and regression problems. We present a novel deep learning-based indoor Wi-Fi path loss modeling approach. Specifically, we propose a local area multi-line scanning algorithm that generates input images based on measurement locations and a floor plan. As the input images contain information regarding the propagation environment between the fixed access points (APs) and measurement locations, a convolutional neural network (CNN) model can be trained to learn the features of the indoor environment and approximate the underlying functions of the Wi-Fi signal propagation. The proposed deep learning-based indoor path loss model can achieve superior performance over 3D ray-tracing methods. The average root mean square error (RMSE) between the predicted and measured received signal strength values in the two scenarios is 4.63 dB.

Keyword: Deep learning; Indoor path loss modeling; Convolutional neural networks

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