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Mobile Information Systems
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2021
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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.
Authors
Aim
Methods
Dalkili et al. [
18
]
To have a new algorithm-based ANFIS for tuning the path loss model
ANFIS
Supachai et al. [
19
]
To propose a multilayer fuzzy logic system (MLFS) for path loss prediction
Multilayer 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 loss
Fuzzy system + linear regression
Sumit et al. [
22
]
To introduce a fuzzy approach on the prediction of path loss
Mamdani fuzzy inference
Bhupuak and Tooprakai [
23
]
The use of K-means clustering and fuzzy logic for the minimization of prediction path loss error
K-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 systems
Fuzzy linear regression
Salman et al. [
25
]
Applied neuro-fuzzy model for the prediction of path loss
ANFIS
Gupta et al. [
20
]
Path loss prediction for current point of base station in a cellular mobile communications
Fuzzy logic
Surajudeen-Bakinde et al. [
27
]
Test ANFIS for path loss prediction
ANFIS
Danladi and Vasira [
28
]
Uses fuzzy logic and spline interpolation to modify the Hata model
Fuzzy 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 bands
Fuzzy logic