Path loss predictions for multi-transmitter radio propagation in VHF bands using Adaptive Neuro-Fuzzy Inference System

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

doi:10.1016/j.jestch.2018.05.013

Cited by 29 publications

(14 citation statements)

References 24 publications

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“…In [13] 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 VHF bands. Through measurements collected at the frequencies 89.3 MHz, 103.5 MHz and 203.25 MHz, it was demonstrated by the results that the path loss model based on ANFIS obtained low values for RMSE, and in similar scenarios, the ANFIS model demonstrated good generalizability with RMSE values.…”

Section: Introductionmentioning

confidence: 99%

Empirical Path Loss Model in City-forest Environment for Mobile Communications

Carvalho¹,

Batalha²,

Neto³

et al. 2021

JCIS

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The feasible choice of a propagation model for a given wireless system depends on environment type among other factors. Thus, it is a crucial decision on radio network planning. This current proposal is a new methodology applied for LTE systems that includes: to find optimal parameters of a propagation model that minimizes Root Mean Square Error (RMSE) and maximizes Grey Relation Grade and Mean Absolute Percentage Error, (GRG-MAPE) in a city-forest environment through the use of metaheuristic optimization such as Cuckoo Search (CS). The results, quantitatively analyzed by RMSE and GRG-MAPE, show a better accuracy of optimized model in comparison with the original version and even with Stanford University Interim (SUI) model.

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Section: Introductionmentioning

confidence: 99%

Empirical Path Loss Model in City-forest Environment for Mobile Communications

Carvalho¹,

Batalha²,

Neto³

et al. 2021

JCIS

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“…e study is limited to the GSM 900 MHz band and a specific city. In [27], a propagation path loss algorithm with the use Mobile Information Systems of one classification out of fuzzy set techniques was proposed, and data set was generated using a multitransmitter (i.e., three different transmitters) radiating radio signals in the VHF bands. e prediction model was developed within an urban terrain in Ilorin, Kwara State, Nigeria.…”

Section: Layermentioning

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%

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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“…The developed ANN model gave the closest match to the experimental data. Path loss prediction for signal propagation using adaptive neuro‐fuzzy interference system (ANFIS) was reported in Surajudeen‐Bakinde et al 22 The ANFIS path loss model employed a five‐layer structure, which was trained using the back propagation algorithm, and the least square algorithm for optimization.…”

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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Path loss predictions for multi-transmitter radio propagation in VHF bands using Adaptive Neuro-Fuzzy Inference System

Cited by 29 publications

References 24 publications

Empirical Path Loss Model in City-forest Environment for Mobile Communications

Empirical Path Loss Model in City-forest Environment for Mobile Communications

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

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

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