Mobile Information Systems / 2021 / Article / Tab 3

Review Article

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

Table 3

Summary of fuzzy logic computational process.

AuthorsAimMethods

Dalkili et al. [18]To have a new algorithm-based ANFIS for tuning the path loss modelANFIS
Supachai et al. [19]To propose a multilayer fuzzy logic system (MLFS) for path loss predictionMultilayer fuzzy logic system (MLFS)
Gupta et al. [20]To propose a better method to predict path loss
Sanu et al. [21]To proffer the use of a BPSK modulated signal to obtain the path lossFuzzy system + linear regression
Sumit et al. [22]To introduce a fuzzy approach on the prediction of path lossMamdani fuzzy inference
Bhupuak and Tooprakai [23]The use of K-means clustering and fuzzy logic for the minimization of prediction path loss errorK-means and fuzzy logic
Supachai and Pisit [24]The use of new upper- and lower-bound models for the line-of-sight prediction of path loss in microwave systemsFuzzy linear regression
Salman et al. [25]Applied neuro-fuzzy model for the prediction of path lossANFIS
Gupta et al. [20]Path loss prediction for current point of base station in a cellular mobile communicationsFuzzy logic
Surajudeen-Bakinde et al. [27]Test ANFIS for path loss predictionANFIS
Danladi and Vasira [28]Uses fuzzy logic and spline interpolation to modify the Hata modelFuzzy logic
Shoewu et al. [29]To develop a new propagation path loss model for different terrains in Lagos in the 900 MHz and 1800 MHz frequency bandsFuzzy logic