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.

翻译：---- 蜂窝覆盖质量估计一直是自组织网络的关键任务。在实际情况中，由于在网络设计和优化期间很少提供基本事实，因此基于深度学习的覆盖质量估计方法无法扩展到大区域。此外，它们不能产生足以充分捕捉单元配置变化的表达嵌入。为了应对这一挑战，我们将任务构建为一种图形表示方式，以便可以应用最先进的图形神经网络（GNN），并展示出了出色的性能。我们提出了一种新的训练框架，旨在同时产生质量良好的单元配置嵌入，以估计多个关键绩效指标（KPIs），同时我们展示，它能够在很少有标记的单元的情况下进行泛化以适应大范围（区域）场景。我们证明，我们的框架产生的准确度与使用大量标记样本训练的模型相当。