A Novel Frame model for Cluster Head Selection and Codeword Detection in the 5G Cellular Networks

4G communications were ruling the entire world with its high-speed network; however, if the users increased, then its speed gets decreased. The 5G model developed, and its rate of data is higher than the 4G frame model. Also, the dense weight node in the cellular network consumed more energy that tends to signal failure. So to make the 5G mobile communications efficient, the present article aimed to develop a novel Grey Wolf (GW) clustering model to choose the cluster head. Moreover, the codeword selection refined by a novel Generalized Intelligent Fuzzy (GIF) mode. Finally, the predictive model as a novel African Buffalo-based Recurrent Model (ABRM) deep learning model developed as the predictive model for continuous multiuser (MU) prediction and monitoring. Subsequently, the data transferred effectively, and its success rate is evaluated with existing models our proposed model gained an excellent outcome by attaining 98.8% of accuracy and reduced complexity rate as 17%.