Machine learning driven clustering for silhouetting 5G network throughput

Compared with previous generations, the 5G enhanced mobile broadband (eMBB) application delivers higher connection, quicker data speeds, and better customer support. Improving data transmission speeds for 5G uplink user equipment (UE) users is the goal of this study. Python is used for data analysis and framework building. This research looks at a 250-m-radius Picocell Base Station (PBS) that can have 15 user equipment (UEs). The position of the user is determined by the cell-range Poisson distribution. The physical base station (PBS), which assesses the state of the signal transmission channel, receives channel state information (CSI) from user equipment (UE). Rayleigh, Rician, free space path, and long-distance route loss models are used in the study. A dataset of channel statuses is generated by the query. There is dynamism in the dataset. K-means clustering is used by UEs to handle service-specific needs. By integrating bandwidth, clustering improves system performance and maximizes the cumulative rate of all user equipment. Channel gain, transmission rate, and minimum service information rate are the characteristics that define UEs. After grouping, users in Cluster 3 had the highest cumulative rate of 9.52 Mbps and an average rate of 7.52 Mbps. In addition to increasing system capacity, bandwidth concatenation satisfied the service needs for every user's equipment (UE). Performance criteria of several clustering models were evaluated, and K-means was found to be the best method. The method was methodically created to satisfy the goals of the study. This research investigates beamforming capabilities and adaptive clustering to improve user fairness and efficiency.