Adaptive Neuro-Fuzzy model for path loss prediction in the VHF band

Salman, Muhammed A.; Popoola, Segun I.; Фарук, Насир; Surajudeen-Bakinde, N. T.; Oloyede, Abdulkarim A.; Olawoyin, Lukman A.

doi:10.1109/iccni.2017.8123768

Cited by 33 publications

(19 citation statements)

References 5 publications

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“…e developed model was compared with the conventional path loss models, and the FLR indicates its improvement in accuracy and, as such, it is suitable for the design of the fourth generation multimedia mobile communication systems exhibiting microwave bands frequency. Salman et al [25] adopted the neuro-fuzzy (NF) model for the prediction of path loss in the Ilorin metropolitan area focusing on the VHF band. Four different routes were used during the measurement to obtain the received signal strength using the NTA Ilorin transmitter, which operates on the frequency of 203.25 MHz; the developed model was alongside compared with widely used empirical path loss models (Hata, Egli, Ecc-33, and COST 231).…”

Section: Layermentioning

confidence: 99%

“…e article reported how the propagation loss through frequency band 30-300 MHz is characterized based on ANN algorithm and its attributes such as the activation functions and training algorithms based on the measured data at 203. 25 MHz.…”

Section: Activation Functionsmentioning

confidence: 99%

“…However, surprisingly the deep learning remains unexploited in propagation path loss prediction notwithstanding it is effectiveness, efficiency, and robustness in solving real world problems. Empirical evidence from the literature has proven that the deep learning architectures such as the deep [34] 2010 ANN ME 0 dB Salman et al [25] 2017 Fuzzy RMSE 5.23 dB Ozdemir et al [40] 2014 ANN RMSE 9.57 dB Olukunle et al [59] 2017 SWARM RMSE 3.030 dB Danladi and Vasira [28] 2018 Fuzzy MAE 1.55 dB & 0.4 dB Al Salameh and Al-Zu'bi [61] 2014 SWARM RMSE 5 dB Fernandes and Soares [53] 2014 Evolutionary ME 3 dB Surajudeen-Bakinde et al [27] 2018 Fuzzy RMSE 4.47 dB Popoola et al [49] 2019 ANN RMSE 0.81 dB Popoola et al [51] 2019 ANN R 0.95…”

Section: Application Of Deep Learningmentioning

confidence: 99%

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Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis

Adebowale

Фарук

Adewole

et al. 2021

Mobile Information Systems

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The importance of wireless path loss prediction and interference minimization studies in various environments cannot be over-emphasized. In fact, numerous researchers have done massive work on scrutinizing the effectiveness of existing path loss models for channel modeling. The difficulties experienced by the researchers determining or having the detailed information about the propagating environment prompted for the use of computational intelligence (CI) methods in the prediction of path loss. This paper presents a comprehensive and systematic literature review on the application of nature-inspired computational approaches in radio propagation analysis. In particular, we cover artificial neural networks (ANNs), fuzzy inference systems (FISs), swarm intelligence (SI), and other computational techniques. The main research trends and a general overview of the different research areas, open research issues, and future research directions are also presented in this paper. This review paper will serve as reference material for researchers in the field of channel modeling or radio propagation and in particular for research in path loss prediction.

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Section: Layermentioning

confidence: 99%

Section: Activation Functionsmentioning

confidence: 99%

Section: Application Of Deep Learningmentioning

confidence: 99%

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Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis

Adebowale

Фарук

Adewole

et al. 2021

Mobile Information Systems

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“…Popoola et al proposes heuristic methods, geospatial and stochastic channel models to predict PL in the VHF and UHF bands for specified routes. The comparative analysis shows that ANN-based PL model has better prediction accuracy and generalization ability than stochastic channel models [36]- [38].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Based Path Loss Prediction for Wireless Communication Network

et al. 2020

IEEE Access

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Accurate path loss (PL) prediction is essential for predicting transmitter coverage and optimizing wireless network performance. Traditional PL models are difficult to cope with the development trend of diversity, time-varying and mass wireless channels. In this work, the most widely used multilayer perceptron (MLP) neural network in artificial neural network (ANN) is employed to accurately predict PL. Three types of environmental features are defined and extracted, which describe the propagation environment only by considering limited environmental types instead of complex 3D environment modeling. Principal component analysis (PCA) is used to generate the low-dimensional environmental features, and eliminate redundant information among similar environmental types. Moreover, the information of base station (BS) and the receiver (Rx), including 3D locations, frequency, the transmitted power of BS, the antenna information, the feeder loss, and the received power of all the locations are obtained from the measurements. Different environmental features are combined with the information of BS and Rx to construct seven datasets for PL prediction models based on MLP neural networks. The impacts of the number of neurons in the hidden layer, the number of hidden layers, the number of training samples, and environmental features on PL prediction models are explored by considering the absolute value of mean error (AME), the mean absolute error (MAE), the standard deviation (STD) of error, the correlation coefficient, and the time ratio, respectively. This work aims to understand the propagation characteristics of radio waves, which can provide a theoretical basis for wireless network optimization and communication system design.

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“…The accuracy and efficiency of path loss prediction models developed with the combination of ANN and extreme learning machine algorithms were investigated in previous works 27,28 . In Salman et al, 29 another ANFIS model was developed in which the effect of the number of membership functions on the overall accuracy of signal propagation model was examined. The ANFIS model was compared with the predictions made by the COST‐231, ECC‐33, and Egli models, and it gave a lower value of the RMSE.…”

Section: Introductionmentioning

confidence: 99%

Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments

Ojo

Imoize

Alienyi³

2020

Int J Communication

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Summary Path loss prediction models occupy a central role in wireless signal propagation because of the continuous need to achieve reliable and high quality of service for subscribers satisfaction. However, the adoption of deterministic and empirical models for pathloss characterization presents a highly contending trade‐off between simplicity and accuracy. On the one hand, empirical models are relatively simple to apply but are mostly inaccurate and inconsistent. Deterministic models are more accurate but quite complex to develop, time‐consuming, and possess nonadaptable characteristics. Toward this end, 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. The contribution of this paper is in threefold. First, experimental data were collected in multitransmitter scenarios via drive test in six base transceiver stations, and the pathloss of the received signal level was derived and analyzed. Two machine learning‐based path loss prediction models were then developed using the measured data as input variables. The developed path loss prediction models are the radial basis function neural network (RBFNN) and the multilayer perception neural network (MLPNN). Further to this, the MLPNN and the RBFNN models were compared with the measured path loss, and the RBFNN appears to be more accurate with lower values of root mean squared errors (RMSEs) in comparison with the MLPNN. Finally, the proposed machine language‐based path loss prediction models (MLPNN and RBFNN) were compared against five existing empirical models, and again, the RBFNN shows the most accurate results.

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Adaptive Neuro-Fuzzy model for path loss prediction in the VHF band

Cited by 33 publications

References 5 publications

Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis

Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis

Artificial Neural Network Based Path Loss Prediction for Wireless Communication Network

Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments

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