In this article, artificial neural networks are applied to data measured on a wireless indoor
mobile communications scenario for 768 MHz transmission. Three multilayer perceptron …

A highly accurate prediction of radio signal power is crucial for planning the coverage of
mobile networks. Currently, a path loss model is most widely used to predict the radio signal …

Accurate received power prediction is important to wireless network planning and
optimization, and appropriate channel modeling approach is highly demanded. The existing …

This study is motivated by the demand for an efficient deep learning-based model that helps
us predict the future link quality for intelligent decision-making systems. In this letter, we …

Paving the path toward the fifth generation (5G) of wireless networks with a huge increase in
the number of user equipment has strengthened public concerns on human exposure to …

Deep learning is applied to predict received power in a 60 GHz band propagation model
from time-series images. Both three-dimensional (3D) convolutional neural network (CNN) …

This study demonstrates the feasibility of proactive received power prediction by leveraging
spatiotemporal visual sensing information towards reliable millimeter-wave (mmWave) …

This paper proposes a method to predict received power in indoor environments
deterministically, which can learn a prediction model from small amount of measurement …

In this paper, we apply and compare various machine learning techniques to predict the
received signal strength (RSS) in cellular communications. We generate the training set …

In this paper, we propose a measurement-based spectrum database using model classifier.
In the radio propagation, path loss is the fundamental factor to recognize the coverage area …