Cognitive network management with optimization using network protocol and machine learning model

被引：1

作者：

Zheng, Yang ^{[1
]}

Song, Ma Chang ^{[1
]}

Qian, Wang ^{[1
]}

Wei, Li ^{[1
]}

机构：

[1] Geely Univ China, Sch Elect Informat Engn, Chengdu 641423, Sichuan, Peoples R China

来源：

COMPUTERS & ELECTRICAL ENGINEERING | 2024年 / 118卷

关键词：

Cognitive network; Optimized beamforming; Game theory; Orthogonal frequency division multiplexing; D2D;

D O I：

10.1016/j.compeleceng.2024.109239

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：11

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