scholarly journals Determination of Neural Network Parameters for Path Loss Prediction in Very High Frequency Wireless Channel

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 150462-150483 ◽  
Author(s):  
Segun I. Popoola ◽  
Abigail Jefia ◽  
Aderemi A. Atayero ◽  
Ogbeide Kingsley ◽  
Nasir Faruk ◽  
...  
2019 ◽  
Vol 8 (12) ◽  
pp. 611-616 ◽  
Author(s):  
Kentaro Saito ◽  
Yongri Jin ◽  
CheChia Kang ◽  
Jun-ichi Takada ◽  
Jenq-Shiou Leu

Author(s):  
Ogbeide K. O. ◽  
Eko Mwenrenren E. J.

The aim of this paper is to present and evaluate artificial neural network model used for path loss prediction of signal propagation in the VHF/UHF spectrum in Edo state.Measurement data obtained from three television broadcasting stations in Edo state, operating at 189.25MHz, 479.25MHz, and 743.25MHz, is used to train and evaluate the artificial neural network. A two layer neural network with one hidden and one output layer is evaluated regarding prediction accuracy and generalization properties. The path loss prediction results obtained by using the artificial neural network model are evaluated against the Hata and Walfisch-Ikegami empirical path loss models .Result analysis shows that the artificial neural network performs well as regards to prediction accuracy and generalization ability. The ANN performed better across all performance measures in comparison to the Hata and Walfisch-Ikegami and Line of Sight models in estimating path loss in vhf/uhf spectrum in Edo state.


2017 ◽  
Vol 61 ◽  
pp. 133-146 ◽  
Author(s):  
Julia Ofure Eichie ◽  
Onyedi David Oyedum ◽  
Moses Oludare Ajewole ◽  
Abiodun Musa Aibinu

Author(s):  
Ogbeide K. O. ◽  
Eko Mwenrenren E. J

The aim of this paper is to present and evaluate artificial neural network model used for path loss prediction of signal propagation in the VHF/UHF spectrum in Edo state.Measurement data obtained from three television broadcasting stations in Edo state, operating at 189.25MHz, 479.25MHz, and 743.25MHz, is used to train and evaluate the artificial neural network. A two layer neural network with one hidden and one output layer is evaluated regarding prediction accuracy and generalization properties. The path loss prediction results obtained by using the artificial neural network model are evaluated against the Hata and Walfisch-Ikegami empirical path loss models .Result analysis shows that the artificial neural network performs well as regards to prediction accuracy and generalization ability. The ANN performed better across all performance measures in comparison to the Hata and Walfisch-Ikegami and Line of Sight models in estimating path loss in vhf/uhf spectrum in Edo state.


2018 ◽  
Vol 23 (3) ◽  
pp. 345-358 ◽  
Author(s):  
Daniele Funaro

If the interior of a conducting cavity (such as a capacitor or a coaxial cable) is supplied with a very high-frequency electric signal, the information between the walls propagates with an appreciable delay, due to the _niteness of the speed of light. The con_guration is typical of cavities having size larger than the wavelength of the injected signal. Such a non rare situation, in practice, may cause a break down of the performances of the device. We show that the classical Coulomb's law and Maxwell's equations do not correctly predict this behavior. Therefore, we provide an extension of the modeling equations that allows for a more reliable determination of the electromagnetic _eld during the evolution process. The main issue is that, even in vacuum (no dielectric inside the device), the fast variation of the signal produces sinks and sources in the electric _eld, giving rise to zones where the divergence is not zero. These regions are well balanced, so that their average in the domain is zero. However, this behavior escapes the usual treatment with classical electromagnetism.


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