Machine learning-based throughput enhancement in fifth-generation networks

Abstract

The 5G enhanced mobile broadband (eMBB) category offers faster data rates, network capacity, and user experiences than prior generations. This research aims to boost the 5G uplink user equipment (UE) user data transfer rate. We use Python to build frameworks and analyze data. A 250-m-radius centre-excited picocell base station (PBS) is investigated to support 15 clients. Cell-range Poisson distribution determines user position. All UEs send channel state information (CSI) to the PBS, which evaluates signal transmission channel conditions. The study uses Rayleigh, Rician, free space path, and long-distance route loss models. This inquiry produces a channel state dataset and then it is formulated dataset is dynamic. For service-specific requirements, UEs use k-means clustering. Clustering concatenates bandwidth, enhancing system efficiency and UE sum rate. The research includes observations from simulation findings, in which UEs are grouped by channel gain, achievable data rate, and minimum service-required data rate. Users in cluster 3 achieve the highest cumulative rate of 9.09 Mbps after clustering with an average of 7.16 Mbps. Bandwidth concatenation increased system capacity, meeting each UE service needs. After evaluating performance criteria for different clustering models, k-means remains the best algorithm for the framework. The methodology was carefully designed to satisfy study goals. This paper investigates beamforming and dynamic clustering to improve user fairness and performance.