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Mobile edge computing based cognitive network security analysis using multi agent machine learning techniques in B5G
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DOI：10.1016/j.compeleceng.2024.109181.png)
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Abstract
En 中文
The proliferation of wireless applications at an exponential rate has made spectrum problems worse. Saturation in the unlicensed frequency spectrum is rapidly increasing as a result of the increasing data rates required by new wireless devices. A proposed solution to this problem is cognitive radio, which allows for the opportunistic use of licenced spectrum in less crowded areas. Cognitive network-based security evaluations using mobile edge computing and a Beyond 5G' (B5G) machine learning (ML) model are the focus of this research. In this case, the security study was carried out using cognitive network data transfer and multi-agent reinforcement encoder neural network and mobile edge computing (MRENN-MEC), a multi-agent reinforcement encoder neural network with mobile edge computing. Scalability, quality of service, throughput, and forecast accuracy are some of the network properties that undergo experimental analysis.
Keywords:
Cognitive network
Security analysis
Mobile edge computing
Machine learning model
B5G
Journal
C
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