Breaking Wireless Propagation Environmental Uncertainty With Deep Learning

Abstract

Wireless propagation loss modeling has gained significant attention due to its critical importance in forthcoming dynamic wireless technologies. Stochastic and map-based propagation models require more information (elevation extension, statistical scattering characteristics) than required by empirical models (i.e., operating frequency, distance between transceivers, and height of the antennas), but such information is not always available. Thus, empirical models are still widely used to evaluate coverage, link budget, and received signal strength. The drawback of empirical models is inaccuracy in highly dynamic transmitter and receiver environments. To reduce the error caused by the use of a single environment, we divide a geographical terrain to employ a specific propagation model in each segment of the wireless link. We enhance a deep learning (DL) encoder-decoder architecture to extract semantic information from satellite imagery to divide an environment into three classes. Our DL architecture achieved a segmentation accuracy of 89.41%, 86.47%, and 87.37% in urban, suburban, and rural classes, respectively. Simulation results indicate that estimating propagation loss with our multi-environment model reduced the root mean square deviation (RMSD) with respect to two publicly available wireless tracing datasets, CU-WART and Portland MetroFi, by 3.79dB and 4.09dB, respectively.