Building an accurate and efficient wireless propagation model is important for predicting
radio wave propagation signals in the target area of the communication area. Different from …

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 …

Electrical load prediction aids electrical producing and allocation firms in planning capacity
and management to ensure that all customers get the energy they need on a consistent …

Businesses are concerned about energy losses. Electronic gadgets have become more
prevalent as a result of their adoption. The frequency with which home electricity usage data …

In low-energy networks, energy consumption is a significant concern. The adjustment of
transmission power can save considerable energy at nodes during communication. The …

Businesses are concerned about energy losses. Electronic gadgets have become more
prevalent as a result of their adoption. The frequency with which home electricity usage data …

This paper proposes a novel rectenna that can estimate the received power level by using
the second-harmonic power generated by the rectenna. The proposed rectenna consists of …

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 …

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

This paper proposes a deep neural network (DNN) based method for the purpose of power-
ground plane impedance modeling. A composite DNN model, which is a combination of two …