  There are vast number of configurable parameters in a Radio Access Telecom
Network. A significant amount of these parameters is configured by Radio Node
or cell based on their deployment setting. Traditional methods rely on domain
knowledge for individual parameter configuration, often leading to sub-optimal
results. To improve this, a framework using a Deep Generative Graph Neural
Network (GNN) is proposed. It encodes the network into a graph, extracts
subgraphs for each RAN node, and employs a Siamese GNN (S-GNN) to learn
embeddings. The framework recommends configuration parameters for a multitude
of parameters and detects misconfigurations, handling both network expansion
and existing cell reconfiguration. Tested on real-world data, the model
surpasses baselines, demonstrating accuracy, generalizability, and robustness
against concept drift.
