Interface Selection and Optimization of Weights using Artificial Neural Network in Heterogeneous Wireless Environment

Given the variety of networks, interfaces, mediums, and other resources accessible in a wireless heterogeneous location today, mobile communication is in a healthy competitive environment. When clients or users have access to numerous interfaces at once, a problem occurs. Users therefore require a c...

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Bibliographic Details
Published in:International journal on smart sensing and intelligent systems 2023-01, Vol.16 (1)
Main Authors: Rani, Monika, Ahuja, Kiran
Format: Article
Language:English
Subjects:
Accessibility
Algorithms
Analytic hierarchy process
Artificial neural networks
Back propagation networks
back propagation neural network
Fuzzy Analytic Hierarchy Process (FAHP)
interface management
Interfaces
Neural networks
Optimization
Parameters
wireless networks
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Summary:Given the variety of networks, interfaces, mediums, and other resources accessible in a wireless heterogeneous location today, mobile communication is in a healthy competitive environment. When clients or users have access to numerous interfaces at once, a problem occurs. Users therefore require a clever or intelligent system to connect them to the finest services based on their needs and choices. Interface management controls the interfaces that are accessible and links the user to the best. The intelligent usage of various radio accesses/interfaces is made possible in this research by interface management with artificial neural networks (ANNs). Various parameters of different networks are used to make the choice. In this paper, a back propagation neural network (BPNN) method for switching between 3G, WLAN, 4G, and 5G networks is suggested. By allocating appropriate weights, the various network properties are employed as selection parameters. Fuzzy Analytic Hierarchy Process (FAHP) is used to initialize the weights, and the BPNN is used to optimize them. In order to obtain the best value, the goal value and the original value are compared, and the difference between them is used as an adjusting value for the weights. The network is trained using backpropagation techniques. The comparison between the proposed algorithm and the current algorithm demonstrates the new method's flexibility and the network's optimal connectivity with a high rate of successful handovers.
ISSN:1178-5608
1178-5608
DOI:10.2478/ijssis-2023-0016