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

[1]  JongKoo Jeon.  Fuzzy and Neural Network Models for Analyses of Piles , 2007 .

[2]  J. D. Parsons,et al.  The Mobile Radio Propagation Channel , 1991 .

[3]  J. Egli.  Radio Propagation above 40 MC over Irregular Terrain , 1957, Proceedings of the IRE.

[4]  T. A. Rahman,et al.  Investigation of path loss prediction in different multi-floor stairwells at 900MHz and 1800MHz , 2014 .

[5]  Hans-Jürgen Zepernick,et al.  Macrocell Path-Loss Prediction Using Artificial Neural Networks , 2010, IEEE Transactions on Vehicular Technology.

[6]  Zia Nadir,et al.  RF Coverage and Pathloss Forecast Using Neural Network , 2013, ICSS.

[7]  Joao M. M. Silva,et al.  Improvement of Outdoor Signal Strength Prediction in UHF Band by Artificial Neural Network , 2016, IEEE Transactions on Antennas and Propagation.

[8]  Felicito S. Caluyo,et al.  Heuristic modelling of outdoor path loss for 9m, 3m and 1.5m antenna at 677 MHz , 2013, 2013 IEEE Conference on Cybernetics and Intelligent Systems (CIS).

[9]  Mehmet Kabak,et al.  The Prediction of Propagation Loss of FM Radio Station Using Artificial Neural Network , 2014 .

[10]  Nasir Faruk,et al.  On the Study of Empirical Path Loss Models for Accurate Prediction of Tv Signal for Secondary Users , 2013 .

[11]  M. Hata,et al.  Empirical formula for propagation loss in land mobile radio services , 1980, IEEE Transactions on Vehicular Technology.

[12]  Nasir Faruk,et al.  Error bounds of empirical path loss models at VHF/UHF bands in Kwara State, Nigeria , 2013, Eurocon 2013.

[13]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[14]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[15]  Qiang Zheng,et al.  Comparison of deterministic and stochastic kinetics for nonlinear systems , 1991 .

[16]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[17]  Rupanwita DasMahapatra.  Optimal power control for cognitive radio in spectrum distribution using ANFIS , 2015, 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES).

[18]  Ayşen Apaydın,et al.  Fuzzy adaptive neural network approach to path loss prediction in urban areas at GSM-900 band , 2010, Turkish Journal of Electrical Engineering and Computer Sciences.

[19]  Faruk Nasir,et al.  PRACTICAL ERROR BOUNDS OF EMPIRICAL MODELS AT VHF/UHF BANDS , 2019 .

[21]  Xuetian Wang,et al.  Measurements and Modeling of Path Loss Over Irregular Terrain for Near-Ground and Short-Range Communications , 2017 .

[22]  Anja Klein,et al.  Comparison and Extension of Existing 3D Propagation Models with Real-World Effects Based on Ray-Tracing , 2014, Wirel. Pers. Commun..

[23]  Roland S. Burns,et al.  Advanced control engineering , 2001 .

[25]  Nasir Faruk,et al.  Improved path-loss model for predicting TV coverage for secondary access , 2014, Int. J. Wirel. Mob. Comput..

[26]  V. S. Abhayawardhana,et al.  Comparison of empirical propagation path loss models for fixed wireless access systems , 2005, 2005 IEEE 61st Vehicular Technology Conference.

[27]  Abiodun Musa Aibinu,et al.  Comparative Analysis of Basic Models and Artificial Neural Network Based Model for Path Loss Prediction , 2017 .

[28]  Tammam A. Benmus,et al.  Neural network approach to model the propagation path loss for great Tripoli area at 900, 1800, and 2100 MHz bands , 2015, 2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA).

[29]  Nasir Faruk,et al.  Clutter and terrain effects on path loss in the VHF/UHF bands , 2018 .