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

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.

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

2019 ◽  
Vol 18 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Nasir Faruk ◽  
N. T. Surajudeen-Bakinde ◽  
Abubakar Abdulkarim ◽  
Segun I. Popoola ◽  
A. Abdulkarim ◽  
...  
Keyword(s):  
Urban Areas ◽  
Communication Systems ◽  
Research Work ◽  
Path Loss ◽  
Theoretical Models ◽  
Anfis Model ◽  
Propagation Models ◽  
Reliable Model ◽  
Loss Prediction ◽  
Path Loss Prediction

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

Development of Radio Propagation Path Loss Model for Kaduna Town, Nigeria Using GMDH Algorithm

2020 ◽  
Vol 5 (10) ◽  
pp. 1253-1259
Author(s):  
Z. M. Abdullahi ◽  
O. U. Okereke ◽  
A. I. Isa ◽  
A. Ozovehe
Keyword(s):  
Path Loss ◽  
Model Fit ◽  
Base Stations ◽  
Radio Propagation ◽  
Propagation Path ◽  
Group Method ◽  
Good Prediction ◽  
Network Cell ◽  
Loss Prediction ◽  
Path Loss Prediction

Radio propagation measurement were acquired at the 900 MHz and 1800 MHz frequency bands from six (6) live base stations (BS1 to BS6) in Kaduna town, Nigeria using an Asus Zenfone enhanced with a network monitoring software (Network Cell Info Lite). The receive signal strength (RSS) measurements were taken from the BSs at a distances of 200 m apart (in dB) until the signal faded out and the measurements were taken for twelve (12) calendar months which covered all seasons of the year, the corresponding path loss were calculated which were subsequently used to develop a propagation path loss prediction model with the Group Method of Data Handling (GMDH) algorithm. However, the results obtained shows very small variations between the model fit (which was the best fit curve from the measured data) and the predictions (which is the forecast). Hence, since the variations between the model fit and the predictions are not wide, with sometime the values of prediction being better than that model fit, the GMDH model is showing good prediction for Kaduna metropolis.

scholarly journals Fusing Diverse Input Modalities for Path Loss Prediction: A Deep Learning Approach

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 30441-30451
Author(s):  
Sotirios P. Sotiroudis ◽  
Panagiotis Sarigiannidis ◽  
Sotirios K. Goudos ◽  
Katherine Siakavara
Keyword(s):  
Deep Learning ◽  
Path Loss ◽  
Learning Approach ◽  
Loss Prediction ◽  
Path Loss Prediction

Calibration of Empirical Models for Path Loss Prediction in Urban Environment

2020 ◽  
pp. 301-310
Author(s):  
Robert O. Abolade ◽  
Dare J. Akintade ◽  
Segun I. Popoola ◽  
Folasade A. Semire ◽  
Aderemi A. Atayero ◽  
...  
Keyword(s):  
Urban Environment ◽  
Path Loss ◽  
Empirical Models ◽  
Loss Prediction ◽  
Path Loss Prediction

scholarly journals Path loss prediction model development in a mountainous forest environment

2021 ◽  
pp. 1-1
Author(s):  
Bilguunmaa Myagmardulam ◽  
Tadachika Nakayama ◽  
Kazuyoshi Takahashi ◽  
Ryu Miura ◽  
Fumie Ono ◽  
...  
Keyword(s):  
Prediction Model ◽  
Model Development ◽  
Path Loss ◽  
Forest Environment ◽  
Loss Prediction ◽  
Path Loss Prediction

scholarly journals Measurement and Prediction of Short-Range Path Loss between 27 and 40 GHz in University Campus Scenarios

2020 ◽  
Author(s):  
Glaucio Ramos ◽  
Carlos Vargas ◽  
Luiz Mello ◽  
Paulo Pereira ◽  
Sandro Gonçalves ◽  
...  
Keyword(s):  
Short Range ◽  
Prediction Models ◽  
Path Loss ◽  
Prediction Method ◽  
University Campus ◽  
Millimetre Wave ◽  
Empirical Prediction ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Path Loss Measurements

Abstract In this paper, we present the results of short-range path loss measurements in the microwave and millimetre wave bands, at frequencies between 27 and 40 GHz, obtained in a campaign inside a university campus in Rio de Janeiro, Brazil. Existing empirical path loss prediction models, including the alpha-beta-gamma (ABG) model and the close-in free space reference distance with frequency dependent path loss exponent (CIF) model are tested against the measured data, and an improved prediction method that includes the path loss dependence on the height di erence between transmitter and receiver is proposed. A fuzzy technique is also applied to predict the path loss and the results are compared with those obtained with the empirical prediction models.

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