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 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 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 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

scholarly journals A Deep Neural Network-Based Multi-Frequency Path Loss Prediction Model from 0.8 GHz to 70 GHz

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5100
Author(s):  
Chi Nguyen ◽  
Adnan Ahmad Cheema
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Deep Neural Network ◽  
Path Loss ◽  
Loss Model ◽  
Path Loss Model ◽  
Proposed Model ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Loss Models

Large-scale fading models play an important role in estimating radio coverage, optimizing base station deployments and characterizing the radio environment to quantify the performance of wireless networks. In recent times, multi-frequency path loss models are attracting much interest due to their expected support for both sub-6 GHz and higher frequency bands in future wireless networks. Traditionally, linear multi-frequency path loss models like the ABG model have been considered, however such models lack accuracy. The path loss model based on a deep learning approach is an alternative method to traditional linear path loss models to overcome the time-consuming path loss parameters predictions based on the large dataset at new frequencies and new scenarios. In this paper, we proposed a feed-forward deep neural network (DNN) model to predict path loss of 13 different frequencies from 0.8 GHz to 70 GHz simultaneously in an urban and suburban environment in a non-line-of-sight (NLOS) scenario. We investigated a broad range of possible values for hyperparameters to search for the best set of ones to obtain the optimal architecture of the proposed DNN model. The results show that the proposed DNN-based path loss model improved mean square error (MSE) by about 6 dB and achieved higher prediction accuracy R2 compared to the multi-frequency ABG path loss model. The paper applies the XGBoost algorithm to evaluate the importance of the features for the proposed model and the related impact on the path loss prediction. In addition, the effect of hyperparameters, including activation function, number of hidden neurons in each layer, optimization algorithm, regularization factor, batch size, learning rate, and momentum, on the performance of the proposed model in terms of prediction error and prediction accuracy are also investigated.

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