  Cellular coverage quality estimation has been a critical task for
self-organized networks. In real-world scenarios, deep-learning-powered
coverage quality estimation methods cannot scale up to large areas due to
little ground truth can be provided during network design & optimization. In
addition they fall short in produce expressive embeddings to adequately capture
the variations of the cells' configurations. To deal with this challenge, we
formulate the task in a graph representation and so that we can apply
state-of-the-art graph neural networks, that show exemplary performance. We
propose a novel training framework that can both produce quality cell
configuration embeddings for estimating multiple KPIs, while we show it is
capable of generalising to large (area-wide) scenarios given very few labeled
cells. We show that our framework yields comparable accuracy with models that
have been trained using massively labeled samples.
