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 …

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 …

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 …

The current and future cellular mobile communication networks generate enormous
amounts of data. Networks have become extremely complex with extensive space of …

Energy-aware system design is an important optimization task for static and mobile Internet
of Things (IoT)-based sensor nodes, especially for highly resource-constrained vehicles …

To receive the highest possible data rate or/and the most reliable connection, the User
Equipment (UE) may want to choose between different networks. However, current LTE and …

Wireless propagation modeling is crucial for designing 5G networks and deploying base
stations. Traditional models are constrained by different propagation environments, and …

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 …