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

arXiv: https://arxiv.org/abs/2208.08039 · HTML: https://mlsanity.com/paper/2208.08039/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 5 of 5)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Deep Learning for THz Drones with Flying Intelligent Surfaces: Beam and Handoff Prediction](https://mlsanity.com/paper/2102.11222/) (2021)
- [Terahertz Technologies to Deliver Optical Network Quality of Experience in Wireless Systems Beyond 5G](https://mlsanity.com/paper/1803.09060/) (2018)
- [Coverage Analysis for 3D Terahertz Communication Systems](https://mlsanity.com/paper/2104.09913/) (2021)
- [Last Meter Indoor Terahertz Wireless Access: Performance Insights and Implementation Roadmap](https://mlsanity.com/paper/1708.02963/) (2017)

## Abstract (arXiv)

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.