Revolutionizing 6G Networks: Implementing Cell-Free Architecture with Advanced Clustering Through Deep Machine Learning

Abstract

The emergence of cell-free marks a revolutionary stride in wireless communications by eliminating traditional boundaries between cells and effectively addressing the demands of heavily populated environments. This research presents an innovative cell-free architecture that leverages advanced clustering techniques, including MeanShift, DBSCAN, KMeans, Affinity Propagation, and ClusterGAN, to enhance resource allocation and network performance. Notably, ClusterGAN demonstrates exceptional promise, achieving superior clustering outcomes with the highest network sum rate and balanced energy efficiency, making it particularly suited for dense and dynamic scenarios. Affinity Propagation also delivers competitive results, highlighting its potential for efficient throughput and resource management. The simulation results confirm the approach’s alignment with 6G objectives, providing a scalable, adaptable, and energy-efficient solution tailored to current wireless networks and data-intensive urban environments.