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Terahertz (THz) wireless networks are expected to catalyze the beyond fifthgeneration (B5G) era. However, due to the directional nature and theline-of-sight demand of THz links, as well as the ultra-dense deployment of THznetworks, a number of challenges that the medium access control (MAC) layerneeds to face are created. In more detail, the need of rethinking userassociation and resource allocation strategies by incorporating artificialintelligence (AI) capable of providing "real-time" solutions in complex andfrequently changing environments becomes evident. Moreover, to satisfy theultra-reliability and low-latency demands of several B5G applications, novelmobility management approaches are required. Motivated by this, this articlepresents a holistic MAC layer approach that enables intelligent userassociation and resource allocation, as well as flexible and adaptive mobilitymanagement, while maximizing systems' reliability through blockageminimization. In more detail, a fast and centralized joint user association,radio resource allocation, and blockage avoidance by means of a novelmetaheuristic-machine learning framework is documented, that maximizes the THznetworks performance, while minimizing the association latency by approximatelythree orders of magnitude. To support, within the access point (AP) coveragearea, mobility management and blockage avoidance, a deep reinforcement learning(DRL) approach for beam-selection is discussed. Finally, to support usermobility between coverage areas of neighbor APs, a proactive hand-overmechanism based on AI-assisted fast channel prediction is~reported.

Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks