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
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
收藏
页数:11
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