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

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Quadri Ramon Adebowale ◽  
Nasir Faruk ◽  
Kayode S. Adewole ◽  
Abubakar Abdulkarim ◽  
Lukman A. Olawoyin ◽  
...  
Keyword(s):  
Computational Intelligence ◽  
Path Loss ◽  
Channel Modeling ◽  
Radio Propagation ◽  
Future Research ◽  
Computational Techniques ◽  
Research Issues ◽  
Main Research ◽  
Loss Prediction ◽  
Path Loss Prediction

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.

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.

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