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.

翻译：太赫兹通信被视作6G网络的关键使能技术。该超高频段丰富的频谱资源有望将网络容量提升至极高数据速率。然而，此类通信极易受遮挡影响，甚至可能中断正在进行的通信。本文深入探讨了通过预测用户与接入点（AP）间可见性以提升太赫兹网络性能的相关性，其核心在于最小化遮挡（即最大化网络可用性）的同时保持较低的重配置开销。我们提出一种创新方法解决该问题：结合神经网络（NN）预测未来用户-AP可见概率，并采用概率阈值进行AP重选以避免不必要的重配置。实验结果表明，当前基于接收信号强度的先进切换机制因难以应对硬遮挡而不适用于太赫兹通信。我们提出的基于神经网络的解决方案显著优于现有机制，证明了该策略作为研究方向的重大价值。