Learning-based visibility prediction for terahertz communications in 6G networks

TL;DR

This paper introduces a neural network-based visibility prediction method for terahertz 6G networks, aiming to reduce blockages and improve network availability with minimal reconfiguration overhead.

Contribution

It presents a novel neural network approach combined with a probability threshold for AP reselection to enhance THz communication reliability.

Findings

NN-based prediction outperforms current handover methods

Significant reduction in unnecessary reconfigurations

Improved network availability in blockage scenarios

Abstract

Terahertz communications are envisioned as a key enabler for 6G networks. The abundant spectrum available in such ultra high frequencies has the potential to increase network capacity to huge data rates. However, they are extremely affected by blockages, to the point of disrupting ongoing communications. In this paper, we elaborate on the relevance of predicting visibility between users and access points (APs) to improve the performance of THz-based networks by minimizing blockages, that is, maximizing network availability, while at the same time keeping a low reconfiguration overhead. We propose a novel approach to address this problem, by combining a neural network (NN) for predicting future user-AP visibility probability, with a probability threshold for AP reselection to avoid unnecessary reconfigurations. Our experimental results demonstrate that current state-of-the-art handover mechanisms based on received signal strength are not adequate for THz communications, since they are ill-suited to handle hard blockages. Our proposed NN-based solution significantly outperforms them, demonstrating the interest of our strategy as a research line.