ANFIS Model for Path Loss Prediction in the GSM and WCDMA Bands in Urban Area

Path loss propagation is a vital concern when designing and planning networks in mobile communication systems. Propagation models such as the empirical, deterministic and theoretical models, which possess complex, inconsistent, time-consuming and non-adaptable features, have proven to be inefficient in designing of wireless systems, thereby resulting in the need for a more reliable model. Artificial Intelligence methods seem to overcome the drawbacks of the propagation models for predicting path loss. In this paper, the ANFIS approach to path loss prediction in the GSM and WCDMA bands is presented for selected urban areas in Nigeria. Furthermore, the effects of the number of Membership Functions (MFs) are investigated. The prediction results indicated that the ANFIS model outperformed the Hata, Cost-231, Egli and ECC-33 models in both Kano and Abuja urban areas. In addition, an increase in the number of MFs conceded an improved RMSE result for the generalized bell-shaped MF. The general performance and outcome of this research work show the efficiency and usefulness of the ANFIS model in improving prediction accuracy over propagation models

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Model-Aided Deep Learning Method for Path Loss Prediction in Mobile Communication Systems at 2.6 GHz

IEEE Access ◽

10.1109/access.2020.2964103 ◽

2020 ◽

Vol 8 ◽

pp. 7925-7936 ◽

Cited By ~ 4

Author(s):

Jakob Thrane ◽

Darko Zibar ◽

Henrik Lehrmann Christiansen

Keyword(s):

Deep Learning ◽

Mobile Communication ◽

Communication Systems ◽

Path Loss ◽

Learning Method ◽

Mobile Communication Systems ◽

Loss Prediction ◽

Path Loss Prediction

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Application of a Composite Differential Evolution Algorithm in Optimal Neural Network Design for Propagation Path-Loss Prediction in Mobile Communication Systems

IEEE Antennas and Wireless Propagation Letters ◽

10.1109/lawp.2013.2251994 ◽

2013 ◽

Vol 12 ◽

pp. 364-367 ◽

Cited By ~ 28

Author(s):

Sotirios P. Sotiroudis ◽

Sotirios K. Goudos ◽

Konstantinos A. Gotsis ◽

Katherine Siakavara ◽

John N. Sahalos

Keyword(s):

Neural Network ◽

Differential Evolution ◽

Mobile Communication ◽

Communication Systems ◽

Differential Evolution Algorithm ◽

Path Loss ◽

Propagation Path ◽

Loss Prediction ◽

Path Loss Prediction ◽

Evolution Algorithm

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Modeling the Distribution of Radiofrequency Intensities from the Digital Terrestrial Television (DTTV) Broadcasting Transmitter in Kampala Metropolitan; Uganda

Asian Journal of Research and Reviews in Physics ◽

10.9734/ajr2p/2020/v3i430130 ◽

2020 ◽

pp. 65-78

Author(s):

Peter Opio ◽

Akisophel Kisolo ◽

Tumps W. Ireeta ◽

Willy Okullo

Keyword(s):

Prediction Models ◽

Reference Signal ◽

Path Loss ◽

Analysis Software ◽

Propagation Models ◽

Received Power ◽

Loss Prediction ◽

Path Loss Prediction ◽

Digital Terrestrial Television ◽

The One

This study presents the modeling of the distribution of RF intensities from the Digital Terrestrial Television (DTTV) broadcasting transmitter in Kampala metropolitan. To achieve this, the performance evaluation of the different path loss propagation models and envisaging the one most suitable for Kampala metropolitan was done by comparing the path loss model values with the measured field Reference Signal Received Power (RSRP) values. The RSRP of the DTTV broadcasting transmitter were measured at operating frequencies of 526 MHz, 638 MHz, 730 MHz and 766 MHz using the Aaronia Spectran HF-6065 V4 spectrum analyzer, Aaronia AG HyperLOG 4025 Antenna at 1.5 m and 2.5 m heights, Aaronia GPS Logger, real time Aaronia MCS spectrum-analysis-software and a T430s Lenovo Laptop. On comparing the measured path loss values with the various path loss prediction model values, results showed that Egli and Davidson models are the most accurate and reliable path loss prediction models for the distribution of DTTV RF intensities in Kampala metropolitan, since their Root Mean Square Error values were the least for both routes.

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Effect of Membership Functions and Data Size on the Performance of ANFIS-Based Model for Predicting Path Losses in the VHF and UHF Bands

Journal of Engineering Research ◽

10.36909/jer.10457 ◽

2021 ◽

Author(s):

Surajudeen-Bakinde N. T. ◽

◽

Nasir Faruk ◽

Abubakar Abdulkarim ◽

Abdulkarim A. Oloyede ◽

...

Keyword(s):

Path Loss ◽

Cellular Systems ◽

Training Data ◽

Base Stations ◽

Membership Functions ◽

Anfis Model ◽

System Parameters ◽

Loss Prediction ◽

Path Loss Prediction ◽

And Training

This paper investigates the effect of number and shape of membership function (MF), and training data size on the performance of ANFIS model for predicting path losses in the VHF and UHF bands in built-up environments. Path loss propagation measurements were conducted in four cities of Nigeria over the cellular and broadcasting frequencies. A total of 17 broadcast transmission and cellular base stations were utilized for this study. From the results obtained, it can be concluded for the broadcasting bands that the generalized bell MF shows better performance with an average RMSE of 3.00 dB across all the routes, followed by gaussian, Pi, trapezoid and triangular MFs in that other with average RMSE values of 3.09 dB, 3.11 dB, 3.16 dB and 3.23 dB respectively. For the cellular systems, Triangular MF outperformed other MFs with the lowest average RMSE. The generalized bell MF was found to be suited for WCDMA band while triangular MF is most suited for GSM band. Furthermore, it can also be concluded that the higher the number of membership functions, the lower the RMSE, whereas, a decrease in the data size leads to a reduction in the RMSE values. The findings of this study would help researchers and network planners to make a more informed decision on choosing appropriate system parameters when modeling ANFIS models for path loss prediction.

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Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion

Applied Sciences ◽

10.3390/app9091908 ◽

2019 ◽

Vol 9 (9) ◽

pp. 1908 ◽

Cited By ~ 16

Author(s):

Yan Zhang ◽

Jinxiao Wen ◽

Guanshu Yang ◽

Zunwen He ◽

Jing Wang

Keyword(s):

Machine Learning ◽

Communication Systems ◽

Classical Model ◽

Path Loss ◽

Measured Data ◽

Machine Learning Algorithms ◽

Support Vector ◽

Loss Prediction ◽

Path Loss Prediction ◽

Data Expansion

Path loss prediction is of great significance for the performance optimization of wireless networks. With the development and deployment of the fifth-generation (5G) mobile communication systems, new path loss prediction methods with high accuracy and low complexity should be proposed. In this paper, the principle and procedure of machine-learning-based path loss prediction are presented. Measured data are used to evaluate the performance of different models such as artificial neural network, support vector regression, and random forest. It is shown that these machine-learning-based models outperform the log-distance model. In view of the fact that the volume of measured data sometimes cannot meet the requirements of machine learning algorithms, we propose two mechanisms to expand the training dataset. On one hand, old measured data can be reused in new scenarios or at different frequencies. On the other hand, the classical model can also be utilized to generate a number of training samples based on the prior information obtained from measured results. Measured data are employed to verify the feasibility of these data expansion mechanisms. Finally, some issues for future research are discussed.

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