A Hybrid Combination of a Convolutional Neural Network with a Regression Model for Path Loss Prediction Using Tiles of 2D Satellite Images

2021 ◽  
Author(s):  
Usman Sammani Sani ◽  
Daphne Teck Ching Lai ◽  
Owais Ahmed Malik
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Satellite Images ◽  
Path Loss ◽  
Hybrid Combination ◽  
Loss Prediction ◽  
Path Loss Prediction

scholarly journals Two-step path loss prediction by artificial neural network for wireless service area planning

2019 ◽  
Vol 8 (12) ◽  
pp. 611-616 ◽  
Author(s):  
Kentaro Saito ◽  
Yongri Jin ◽  
CheChia Kang ◽  
Jun-ichi Takada ◽  
Jenq-Shiou Leu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Path Loss ◽  
Service Area ◽  
Wireless Service ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Artificial Neural ◽  
Area Planning

scholarly journals Path-Loss Prediction for UHF/VHF Signal Propagation in Edo State: Neural Network Approach

2016 ◽  
Vol 1 (2) ◽  
pp. 77-84
Author(s):  
Ogbeide K. O. ◽  
Eko Mwenrenren E. J.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Artificial Neural Network Model ◽  
Path Loss ◽  
Signal Propagation ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Edo State ◽  
Artificial Neural

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.

scholarly journals COMPARATIVE ANALYSIS OF BASIC MODELS AND ARTIFICIAL NEURAL NETWORK BASED MODEL FOR PATH LOSS PREDICTION

2017 ◽  
Vol 61 ◽  
pp. 133-146 ◽  
Author(s):  
Julia Ofure Eichie ◽  
Onyedi David Oyedum ◽  
Moses Oludare Ajewole ◽  
Abiodun Musa Aibinu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Comparative Analysis ◽  
Path Loss ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Artificial Neural

Path-Loss Prediction for UHF/VHF Signal Propagation in Edo State: Neural Network Approach

2016 ◽  
Vol 1 (2) ◽  
pp. 77-84
Author(s):  
Ogbeide K. O. ◽  
Eko Mwenrenren E. J
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Artificial Neural Network Model ◽  
Path Loss ◽  
Signal Propagation ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Edo State ◽  
Artificial Neural

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.

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 ◽  
...  
Keyword(s):  
Neural Network ◽  
High Frequency ◽  
Path Loss ◽  
Wireless Channel ◽  
Very High Frequency ◽  
Network Parameters ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Very High
Sign in / Sign up

Export Citation Format

Share Document