Artificial neural network models for radiowave propagation in tunnels

Abstract

The authors present a machine learning approach for the extraction of radiowave propagation models in tunnels. To that end, they discuss three challenges related to the application of machine learning to general wireless propagation problems: how to efficiently specify the input to the model, which learning method to use and what output functions to seek. The input that any propagation modelling tool (be it a ray‐tracer, a full‐wave method or a parabolic equation solver) uses, can be considered as visual, in the form of an image or a point cloud of the environment under consideration. Therefore, they propose an artificial neural network structure that generalises well to various geometries. The desired output can be values of the electromagnetic field components across the channel or just a path loss model. They apply these ideas to the case of arched tunnels for the first time. They consider cases where the geometric parameters of the tunnel, the position of the receiver and the frequency of operation are parts of a model trained by a vector parabolic equation solver. The model is evaluated using solver‐generated as well as measured data. The numerical results demonstrate that this approach combines computational efficiency with high accuracy.

An Overview of Machine Learning Techniques for Radiowave Propagation Modeling

“…There have been papers focusing on urban environments, such as [12], [13], rural, such as [14], [15], or even a mix of different outdoor environments, such as [16]. Special environments such as roads, mines and subway tunnels have also been considered [17]- [19].…”

“…The method assumes a paraxial approximation with respect to the direction of propagation of the wave. Therefore, it is often used in simulating enclosed environments that have waveguiding characteristics [19], or terrestrial propagation scenarios [15].…”

Analysis and Modeling of Propagation in Tunnel at 3.7 and 28 GHz

“…In addition, the current radio propagation models typically predict the path loss, power delay profile, or delay spread for specific transmitter and receiver locations [4]. However, it is generally accepted that the propagation inside a tunnel is distinctly different when compared to other types of propagation media, such as outdoor, outdoor to indoor, indoor to outdoor, or indoor-to-indoor radio wave propagation [5]. The fundamental difference in the tunnel is that the radio wave is enclosed by the blocking surface (of the tunnel) through which the refracted wave cannot reach the receiver, and as such a propagated signal is received in other cases through a penetration loss at the tunnel blocking plane.…”

“…In contrast to open-air propagation, tunnel propagation includes electromagnetic waves in an enclosed environment [9]. A leaky feeder communication system can be deployed inside confined locations, in particular inside road or rail tunnels [5]. The cable is leaky in the sense that it includes gaps or slots in its outer conductor that allow radio signals to leak into or out of the cable along its entire length.…”

AI-Empowered Propagation Prediction and Optimization for Reconfigurable Wireless Networks

“…The proposed channel model is based on artificial neural networks (ANNs), including feed-forward neural network (FNN) and radial basis function neural network (RBF-NN). The authors of [41] presented a machine learning approach for the extraction of radio wave propagation models in tunnels. In [42], the authors developed a channel state information (CSI) extraction tool and investigated the performance of channel prediction with a deep learning approach and an autoregression (AR) approach based on realistic measurement data in vehicular environments.…”