Ray-tracing driven ann propagation models for indoor environments at 28 GHz
A Seretis, T Hashimoto, K Zeng… - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
2020 IEEE International Symposium on Antennas and Propagation and …, 2020•ieeexplore.ieee.org
Ray-tracing is widely used for radio propagation modeling of indoor environments, such as
hallways and offices. Shooting and bouncing ray-tracing methods are faster than fullwave
methods in such environments, especially as the frequency of operation increases following
the new 5G specifications. Still, a machine learning approach can generalize a few ray-
traced points into full signal strength maps of arbitrary resolution. In this paper, a feedforward
standard artificial neural network is trained by ray-tracing data at 28 GHz to predict signal …
hallways and offices. Shooting and bouncing ray-tracing methods are faster than fullwave
methods in such environments, especially as the frequency of operation increases following
the new 5G specifications. Still, a machine learning approach can generalize a few ray-
traced points into full signal strength maps of arbitrary resolution. In this paper, a feedforward
standard artificial neural network is trained by ray-tracing data at 28 GHz to predict signal …
Ray-tracing is widely used for radio propagation modeling of indoor environments, such as hallways and offices. Shooting and bouncing ray-tracing methods are faster than fullwave methods in such environments, especially as the frequency of operation increases following the new 5G specifications. Still, a machine learning approach can generalize a few ray-traced points into full signal strength maps of arbitrary resolution. In this paper, a feedforward standard artificial neural network is trained by ray-tracing data at 28 GHz to predict signal strength in a Γ -shaped corridor. The network's accuracy in reconstructing the actual signal levels is on par with that of the ray-tracer.
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