Comparative Analysis of Basic Models and Artificial Neural Network Based Model for Path Loss Prediction
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TLDR
Propagation parameters, such as distance between transmitting and receiving antennas, transmitting power and terrain elevation, were used as inputs to Artificial Neural Network for the development of an ANN based path loss model, which performed better than basic empirical path loss models considered.Abstract:
Propagation path loss models are useful for the prediction of received signal strength at a given distance from the transmitter; estimation of radio coverage areas of Base Transceiver Stations (BTS); frequency assignments; interference analysis; handover optimisation; and power level adjustments. Due to the differences in: environmental structures; local terrain profiles; and weather conditions, path loss prediction model for a given environment using any of the existing basic empirical models such as the Okumura-Hata’s model has been shown to differ from the optimal empirical model appropriate for such an environment. In this paper, propagation parameters, such as distance between transmitting and receiving antennas, transmitting power and terrain elevation, using sea level as reference point, were used as inputs to Artificial Neural Network (ANN) for the development of an ANN based path loss model. Data were acquired in a drive test through selected rural and suburban routes in Minna and environs as dataset required for training ANN model. Multilayer perceptron (MLP) network parameters were varied during the performance evaluation process, and the weight and bias values of the best performed MLP network were extracted for the development of the ANN based path loss models for two different routes, namely rural and suburban routes. The performance of the developed ANN based path loss model was compared with some of the existing techniques and modified techniques. Using Root Mean Square Error (RMSE) obtained between the measured and the model outputs as a measure of performance, the newly developed ANN based path loss model performed better than the basic empirical path loss models considered such as: Hata; Egli; COST-231; Ericsson models and modified path loss approach.read more
Citations
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Journal ArticleDOI
Determination of Neural Network Parameters for Path Loss Prediction in Very High Frequency Wireless Channel
Segun I. Popoola,Abigail Jefia,Aderemi A. Atayero,Ogbeide Kingsley,Nasir Faruk,Olasunkanmi F. Oseni,Robert O. Abolade +6 more
TL;DR: An extensive investigation was conducted to determine the most appropriate neural network parameters for path loss prediction in Very High Frequency (VHF) band and showed that ANN-based path loss model has better prediction accuracy and generalization ability than the empirical models.
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Path Loss Predictions in the VHF and UHF Bands Within Urban Environments: Experimental Investigation of Empirical, Heuristics and Geospatial Models
Nasir Faruk,Segun I. Popoola,N. T. Surajudeen-Bakinde,Abdulkarim A. Oloyede,Abubakar Abdulkarim,Lukman A. Olawoyin,Maaruf Ali,Carlos T. Calafate,Aderemi A. Atayero +8 more
TL;DR: The findings of this study will help radio network engineers to achieve efficient radio coverage estimation; determine the optimal base station location; make a proper frequency allocation; select the most suitable antenna; and perform interference feasibility studies.
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Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments
TL;DR: This paper proposes to address the problems associated with the existing models (empirical and deterministic) through the introduction of machine learning algorithms to path loss predictions by developing two machine learning‐based path loss prediction models.
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Cellular Communications Coverage Prediction Techniques: A Survey and Comparison
TL;DR: The purpose of this paper is to survey the existing techniques and mechanisms which can be addressed in this domain and provide comparative analysis to aid the planning and implementation of the cellular networks.
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Path loss predictions for multi-transmitter radio propagation in VHF bands using Adaptive Neuro-Fuzzy Inference System
N. T. Surajudeen-Bakinde,Nasir Faruk,Segun I. Popoola,Muhammed A. Salman,Abdulkarim A. Oloyede,Lukman A. Olawoyin,Carlos T. Calafate +6 more
TL;DR: A new path loss prediction model is developed based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) for multi-transmitter radio propagation scenarios and applicable to the Very High Frequency (VHF) bands, offering desirable advantages in terms of simplicity, high prediction accuracy, and good generalization ability.
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