Corpus ID: 237571872

An empirical study on using CNNs for fast radio signal prediction

  title={An empirical study on using CNNs for fast radio signal prediction},
  author={Ozan Ozyegen and Sanaz Mohammadjafari and Karim El Mokhtari and Mucahit Cevik and Jonathan Ethier and Ayse Basar},
Accurate radio frequency power prediction in a geographic region is a computationally expensive part of finding the optimal transmitter location using a ray tracing software. We empirically analyze the viability of deep learning models to speed up this process. Specifically, deep learning methods including CNNs and UNET are typically used for segmentation, and can also be employed in power prediction tasks. We consider a dataset that consists of radio frequency power values for five different… Expand


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Neural network-based path loss prediction for digital TV macrocells
  • Joel C. Delos Angeles, E. Dadios
  • Computer Science
  • 2015 International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)
  • 2015
This paper aims to propose and ascertain the viability of using an alternative neural network (NN) model to predict path loss, and shows that the neural network-based propagation model is shown to give more accurate results compared to more familiar propagation models, while having the advantage of adaptability to arbitrary environments. Expand
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Dominant Path Prediction Model for Urban Scenarios
Currently, for the planning of wireless cellular networks in urban scenarios either empirical (direct ray or over rooftop ray) or ray-optical (ray tracing) propagation models are used. In this paperExpand