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

TL;DR

This paper proposes an AI-driven holistic MAC layer framework for THz wireless networks that enhances user association, resource allocation, and mobility management to meet ultra-reliable, low-latency requirements.

Contribution

It introduces a novel metaheuristic-machine learning framework for joint user association and blockage avoidance, and a DRL-based beam selection and proactive hand-over mechanism.

Findings

Association latency reduced by approximately three orders of magnitude

Enhanced blockage minimization improves network reliability

AI-based mobility management supports seamless user transitions

Abstract

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