scholarly journals Artificial Neural Network Based Path Loss Prediction for Wireless Communication Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 199523-199538 ◽  
Author(s):  
Lina Wu ◽  
Danping He ◽  
Bo Ai ◽  
Jian Wang ◽  
Hang Qi ◽  
...  
Keyword(s):  
Neural Network ◽  
Communication Network ◽  
Artificial Neural Network ◽  
Wireless Communication ◽  
Path Loss ◽  
Wireless Communication Network ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Artificial Neural

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 Path Loss Prediction Based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network, and Gaussian Process

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1927 ◽  
Author(s):  
Han-Shin Jo ◽  
Chanshin Park ◽  
Eunhyoung Lee ◽  
Haing Kun Choi ◽  
Jaedon Park
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Gaussian Process ◽  
Path Loss ◽  
Principal Component ◽  
Component Analysis ◽  
Machine Learning Techniques ◽  
Shadowing Effect ◽  
Artificial Neural

Although various linear log-distance path loss models have been developed for wireless sensor networks, advanced models are required to more accurately and flexibly represent the path loss for complex environments. This paper proposes a machine learning framework for modeling path loss using a combination of three key techniques: artificial neural network (ANN)-based multi-dimensional regression, Gaussian process-based variance analysis, and principle component analysis (PCA)-aided feature selection. In general, the measured path loss dataset comprises multiple features such as distance, antenna height, etc. First, PCA is adopted to reduce the number of features of the dataset and simplify the learning model accordingly. ANN then learns the path loss structure from the dataset with reduced dimension, and Gaussian process learns the shadowing effect. Path loss data measured in a suburban area in Korea are employed. We observe that the proposed combined path loss and shadowing model is more accurate and flexible compared to the conventional linear path loss plus log-normal shadowing model.

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