Effect of Membership Functions and Data Size on the Performance of ANFIS-Based Model for Predicting Path Losses in the VHF and UHF Bands
Key words: ANFIS; Membership Function; Path loss; VHF; UHF
Today, the world is technology-driven and so of these technologies are driven using wireless systems. Signal coverage and quality of service are pertinent for network providers and path loss prediction is very important in the design and planning of these systems. However, the inefficiency of conventional propagation models, such as empirical, deterministic and theoretical models has been established in previous research works. The machine learning methods have recently been applied for prediction of path losses. The Adaptive Neuro-Fuzzy Inference System (ANFIS) technique is one of the machine learning techniques that has been successfully employed to predict path losses in different environments. However, the performance of the ANFIS-based models in terms of the computational complexity and accuracy depends on the selection of the appropriate system parameters. This paper, therefore, 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 modelling ANFIS models for path loss prediction.