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Deep learning approaches for fast radio signal prediction

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Document pages: 19 pages

Abstract: The aim of this work is the prediction of power coverage in a dense urbanenvironment given building and transmitter locations. Conventionallyray-tracing is regarded as the most accurate method to predict energydistribution patterns in the area in the presence of diverse radio propagationphenomena. However, ray-tracing simulations are time consuming and requireextensive computational resources. We propose deep neural network models tolearn from ray-tracing results and predict the power coverage dynamically frombuildings and transmitter properties. The proposed UNET model with stridedconvolutions and inception modules provide highly accurate results that areclose to the ray-tracing output on 32x32 frames. This model will allowpractitioners to search for the best transmitter locations effectively andreduce the design time significantly.

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