  Wireless applications that use high-reliability low-latency links depend
critically on the capability of the system to predict link quality. This
dependence is especially acute at the high carrier frequencies used by mmWave
and THz systems, where the links are susceptible to blockages. Predicting
blockages with high reliability requires a large number of data samples to
train effective machine learning modules. With the aim of mitigating data
requirements, we introduce a framework based on meta-learning, whereby data
from distinct deployments are leveraged to optimize a shared initialization
that decreases the data set size necessary for any new deployment. Predictors
of two different events are studied: (1) at least one blockage occurs in a time
window, and (2) the link is blocked for the entire time window. The results
show that an RNN-based predictor trained using meta-learning is able to predict
blockages after observing fewer samples than predictors trained using standard
methods.
