The Prediction of Propagation Loss of FM Radio Station Using Artificial Neural Network

Özdemir, Ali Rıza; Alkan, Mustafa; Kabak, Mehmet; Gulsen, Mehmet; Sazlı, Murat H.

doi:10.4236/jemaa.2014.611036

Cited by 8 publications

(9 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Artificial neural network and application of adaptive evolutionary algorithms Ozdemir et al [40] To use the Levenberg-Marquardt algorithm for studying the propagation loss of FM radio stations Levenberg-Marquardt algorithm ANN Dela Cruz and Caluyo [41] To develop a statistical path loss model by measuring indoor losses using a fixed portable indoor antenna ANN…”

Section: Authors Aim Methodsmentioning

confidence: 99%

“…Results indicated that the proposed model prediction achieves a satisfactory accuracy compared with the output from the ray-tracing model in the same environment. e authors in [40] relied on the Levenberg-Marquardt algorithm as one of the ANN models to predict propagation path loss of an FM radio station. e results obtained from the Levenberg-Marquardt algorithm were compared with ITU-R 1546 and Epstein-Peterson models.…”

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%

See 2 more Smart Citations

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

Adebowale

Фарук

Adewole

et al. 2021

Mobile Information Systems

View full text Add to dashboard Cite

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.

show abstract

Section: Authors Aim Methodsmentioning

confidence: 99%

Section: Activation Functionsmentioning

confidence: 99%

Section: Application Of Deep Learningmentioning

confidence: 99%

See 1 more Smart Citation

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

Adebowale

Фарук

Adewole

et al. 2021

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…Neural networks have been very effective for modeling and for characterisation of complex systems for a number of applications [ 29 , 30 ]. The successful application of ANN to a number of scientific and engineering problems abounds [ 31 , 32 , 33 , 34 , 35 ], including the prediction of signal strength in the VHF and UHF bands [ 2 , 36 , 37 , 38 , 39 , 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural network modeling techniques to signal strength computation

Igwe

Oyedum

Aibinu

et al. 2021

Heliyon

View full text Add to dashboard Cite

This paper presents development of artificial neural network (ANN) models to compute received signal strength (RSS) for four VHF (very high frequency) broadcast stations using measured atmospheric parameters. The network was trained using Levenberg-Marquardt back-propagation (LMBP) algorithm. Evaluation of different effects of activation functions at the hidden and output layers, variation of number of neurons in the hidden layer and the use of different types of data normalisation were systematically applied in the training process. The mean and variance of calculated MSE (mean square error) for ten different iterations were compared for each network. From the results, the ANN model performed reasonably well as computed signal strength values had a good fit with the measured values. The computed MSE were very low with values ranging between 0.0027 and 0.0043. The accuracy of the trained model was tested on different datasets and it yielded good results with MSE of 0.0069 for one dataset and 0.0040 for another dataset. The measured field strength was also compared with ANN and ITU-R P. 526 diffraction models and a strong correlation was found to exist between the measured field strength and ANN computed signals, but no correlation existed between the measured field strength and the predicted field strength from diffraction model. ANN has thus proved to be a useful tool in computing signal strength based on atmospheric parameters.

show abstract

“…Machine learning has attracted much attention since it can effectively find specific rules from massive data and further make predictions. The authors in [17,18] proved that machine learning algorithms have good performance on path loss prediction in traditional communication systems. However, for the reason of the altitudes, the radio propagation characteristics of the UAV communications are different from those on the ground.…”

Section: Introductionmentioning

confidence: 99%

Machine‐learning‐based prediction methods for path loss and delay spread in air‐to‐ground millimetre‐wave channels

Yang

Zhang

et al. 2019

IET Microwaves, Antennas & Propagation

View full text Add to dashboard Cite

The unmanned aerial vehicles (UAVs) have been widely applied in various fields due to their advantages like high mobility and low cost. Reliable communication is the premise to ensure the connectivity between UAV nodes. To provide reasonable references for the design, deployment, and operation of UAV communication systems, the precise prediction of radio channel parameters are required. In this study, the authors propose prediction methods for path loss and delay spread in air‐to‐ground millimetre‐wave channels based on machine learning. Random forest and K‐nearest‐neighbours are the algorithms employed in the methods. Then, a feature selection scheme is proposed to further improve the prediction accuracy and generalisation performance of the machine‐learning‐based methods. Generally, machine learning algorithms require massive data for training purpose. However, measuring data is time‐consuming and costly, especially when the scenario or frequency changes. Therefore, transfer learning methods are introduced to predict path loss with limited data. The proposed methods for path loss prediction are compared to Okumura–Hata and COST‐231 Hata models. The lognormal distribution is the contrast model in delay spread prediction. Based on the data generated by ray‐tracing software, the new methods have a smaller root mean square errors than contrast models.

show abstract

The Prediction of Propagation Loss of FM Radio Station Using Artificial Neural Network

Cited by 8 publications

References 7 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

Application of artificial neural network modeling techniques to signal strength computation

Machine‐learning‐based prediction methods for path loss and delay spread in air‐to‐ground millimetre‐wave channels

Contact Info

Product

Resources

About