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Cognitive network management with optimization using network protocol and machine learning model
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DOI：10.1016/j.compeleceng.2024.109239.png)
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Abstract
En 中文
The significance of cognitive radios in satisfying the growing demand for bandwidth in wireless networks is anticipated to be significant. An innovative method for cognitive network beam- forming and interference control based on machine learning and an intelligent network protocol is suggested in this study. A Reinforcement Nash Equilibrium Game Theory (RNEGT) model is used to control network interference, and the Spatiotemporal Non-Orthogonal Multiple Access (ST-NOMA) technique is used for beamforming inside the network. Throughput, spectrum efficiency, stability, Mean Square Error (MSE), and Signal-to-Interference and Noise Ratio (SINR) are some of the metrics used in experimental study. Examined approach can optimize received SINR at destinations while maintaining interference + noise power below a certain threshold by employing the suggested relay selection and a cooperative beamforming (CBF) mechanism in each cluster.
Keywords:
Cognitive network
Optimized beamforming
Game theory
Orthogonal frequency division multiplexing
D2D
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