Optimization of the Scheduling Problem in Cell-Free Massive MIMO Communication Systems

Abstract

[Abstract] This work addresses the selection of subsets of access points (APs) to serve users cooperatively in Cell-Free Massive MIMO (Multiple-Input Multiple-Output) systems. Traditional cellular architectures suffer from coverage and interference issues at cell edges. In contrast, Cell-Free networks distribute simpler APs uniformly across the area, enhancing spectral efficiency and connection quality. We simulate realistic communication scenarios to train a Deep Contextual Bandits (DCB) model that optimizes AP assignment based on user channel gains and interference. Alternative data-driven approaches, such as loss-based models and fuzzy clustering, are also developed and evaluated. Results show that DCB offers scalable, adaptive performance for next-generation wireless networks like B5G and 6G.