<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

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

Department of Electronics & Communication Engineering

DAV Institute of Engineering & Technology, Jalandhar, IKG Punjab Technical University

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Given the variety of networks, interfaces, mediums, and other resources accessible in a wireless heterogeneous location today, mobile communication is in a...

Application	DS	L	BW	PLR	Jitter	Total
Video	0.3101	0.1661	0.2912	0.0519	0.1808	1
Audio	0.4427	0.2843	0.1127	0.0639	0.0964	1
Data transfer	0.5232	0.1924	0.1331	0.1152	0.0361	1
Web browsing	0.3686	0.3743	0.1242	0.0868	0.0461	1

Network	Obtained output (normalized values)/s	Delivered output (normalized values)/s	% Throughput
3G	0.8817	0.9524	92.58
WLAN	0.896	0.966	92.75
4G	0.9243	0.955	96.79
5G	0.9182	0.988	92.94

Networks	Network download speed (Mbps)	Latency (ms)	Bandwidth	PLR (%)	Jitter (ms)	Target output (maximum download speed) Mbps
3G	144 kbps–2 Mbps	30–200	0.1 Mbps–3 Mbps	2–10	10–30	3–7.2
WLAN	1 Mbps–8 Mbps	80–300	2 Mbps–10 Mbps	4–15	30–80	450–600
4G	15 Mbps–90 Mbps	20–150	200 Mbps–1 Gbps	4–20	15–40	20–200
5G	150 Mbps–10 Gbps	1–10	1 Gbps–10 Gbps	0–10	10–20	1000–10,000

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

Article Category: Article

Online veröffentlicht: 15. Dez. 2023

Seitenbereich: -

Eingereicht: 31. Mai 2023

DOI: https://doi.org/10.2478/ijssis-2023-0016

Schlüsselwörter
Fuzzy Analytic Hierarchy Process (FAHP), interface management, back propagation neural network, wireless networks

© 2023 Monika Rani et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1:

Figure 2:

Figure 3:

Figure 4:

Figure 5:

Figure 6:

Figure 7:

Figure 8:

Figure 9:

Figure 10:

Figure 11:

Figure 12:

Normalized weights for different applications

Throughput of networks

Parameters of different networks [24, 25]

Mean square error of available networks

Network	Mean Square Error
3G	0.005272
WLAN	0.00499
4G	0.004527
5G	0.008507

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

Article Category: Article

Online veröffentlicht: 15. Dez. 2023

Seitenbereich: -

Eingereicht: 31. Mai 2023

DOI: https://doi.org/10.2478/ijssis-2023-0016

SchlüsselwörterFuzzy Analytic Hierarchy Process (FAHP), interface management, back propagation neural network, wireless networks

© 2023 Monika Rani et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1:

Figure 2:

Figure 3:

Figure 4:

Figure 5:

Figure 6:

Figure 7:

Figure 8:

Figure 9:

Figure 10:

Figure 11:

Figure 12:

Normalized weights for different applications

Throughput of networks

Parameters of different networks [24, 25]

Mean square error of available networks

Schlüsselwörter
Fuzzy Analytic Hierarchy Process (FAHP), interface management, back propagation neural network, wireless networks