| Autor: |
Kiran, Kondapalli Sai, Bharathi, P. Shyamala |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 2871 Issue 1, p1-6, 6p |
| Abstrakt: |
This project aims to compare coalition game theory with state-of-the-art artificial neural networks in order to optimize the data transmission rate for secondary users of cognitive radio networks. Actions to Take and Items to Bring: The SIMATS system that conducted the desired operation. This experiment made use of two groups. Twenty people were divided up between the two groups at random. Technologies for spectrum identification that employ innovative artificial neural networks were included in Group 1. The cognitive radio network's major actors prepared the second group's samples. To evaluate the Coalition Game Theory–based algorithm, we used 80% of G power. The difference between the two data sets is 0.018 (p 0.05). The finished item: While coalition game theory yielded an accuracy of 89.0815, the simulation results showed that the artificial neural network achieved an average accuracy of 96. 7347. In the end Artificial neural networks optimize throughput in secondary user communication more accurately and precisely than Coalition game theory. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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