Mobile Information Systems / 2021 / Article / Tab 4

Review Article

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

Table 4

Summary of artificial neural networks contributions.


Popescu et al. [35]To study the application of neural networks to the prediction of propagation path loss in urban and suburban environmentsFeed forward neural networks
Sotiroudis et al. [36]To propose an alternative neural network algorithm for the prediction of propagation path loss in urban environmentsANN
Oustlin et al. [34]To analyze ANN models used for macrocell path loss estimationANN
Kalakh et al. [37]To present an ultrawide band propagation channel modeling with neural networks in a mine environmentANN
Zaarour et al. [38]To use MLP and RBF artificial neural networks to study ultrawide band communication channelsANN: multilayer perception (MLP) and radial basis function (RBF)
Sotiroudis et al. [39]To produce an alternative procedure for predicting propagation path loss in urban environmentsArtificial neural network and application of adaptive evolutionary algorithms
Ozdemir et al. [40]To use the Levenberg–Marquardt algorithm for studying the propagation loss of FM radio stationsLevenberg–Marquardt algorithm ANN
Dela Cruz and Caluyo [41]To develop a statistical path loss model by measuring indoor losses using a fixed portable indoor antennaANN
Nadir and Idrees Ahmad [42]To address the applicability of the Okumura-Hata model in GSM frequency band of 890–960 MHzANN
Delos Angeles and Dadios [43]To predict path loss for TV transmission using alternative neural networks, and ascertain the proposed model viabilityANN
Benmus et al. [44]To predict the propagation path loss with an empirical model at the capital city of LibyaANN
Ofure et al. [33]To use a three-stage approach in the determination of GSM Rx level from atmospheric parametersANN
Eichie et al. [45]To develop an ANN-based path loss estimation model for rural and urban areasANN
Moazenni [46]To study the relation between the path loss propagation delay and the atmosphere parameter with a neural modelANN
Wu et al. [47]To propose a new artificial neural network prediction model for railway environmentsANN