RT Journal Article
T1 Learning-based visibility prediction for terahertz communications in 6G networks
A1 Fondo Ferreiro, Pablo
A1 Lopez Bravo, Cristina
A1 González Castaño, Francisco Javier
A1 Gil Castiñeira, Felipe Jose
A1 Candal Ventureira, David
K1 3325 Tecnología de las Telecomunicaciones
AB 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.
PB Computer Communications
SN 01403664
YR 2024
FD 2024-12
LK http://hdl.handle.net/11093/7512
UL http://hdl.handle.net/11093/7512
LA eng
NO Computer Communications, 228, 107956 (2024)
NO Xunta de Galicia | Ref. ED481B-2022-019
DS Investigo
RD 25-abr-2025