Downlink and Uplink User Selection in MU-MIMO: A Real-Time Unsupervised Probabilistic Framework

User selection is an important component of the Radio Resource Management of fully digital Multi-User Multiple-Input Multiple-Output (MU-MIMO) systems and its efficiency is critical to the performance of these systems. In this paper, we propose a novel framework called,Dynamic Probabilistic User Selection (DPUS), that formulates user selection as a probabilistic inference task using Markov Random Fields and Loopy Belief Propagation. Unlike existing methods, DPUS operates in an online unsupervised manner, eliminates the need for labeled data, and integrates fairness constraints with channel-awareness. Importantly, DPUS can apply to both the downlink and the uplink. Extensive simulations conducted in a large range of deployment scenarios show that DPUS consistently outperforms state-of-the-art heuristics in terms of fairness, scalability, and runtime. DPUS provides a robust solution for the efficient operation of fully digital MU-MIMO systems, paving the way for next-generation wireless networks.