  The roll out of new mobile network generations poses hard challenges due to
various factors such as cost-benefit tradeoffs, existing infrastructure, and
new technology aspects. In particular, one of the main challenges for the 5G
deployment lies in optimal 5G radio coverage while accounting for diverse
service performance metrics. This paper introduces a Deep Learning-based
approach to assist in 5G radio planning by utilizing data from
previous-generation cells. Our solution relies on a custom graph representation
to leverage the information available from existing cells, and employs a Graph
Neural Network (GNN) model to process such data efficiently. In our evaluation,
we test its potential to model the transition from 4G to 5G NSA using
real-world data from a UK mobile network operator. The experimental results
show that our solution achieves high accuracy in predicting key performance
indicators in new 5G cells, with a Mean Absolute Percentage Error (MAPE)~<17\%
when evaluated on samples from the same area where it was trained. Moreover, we
test its generalization capability over various geographical areas not included
in the training, achieving a MAPE~<19\%. This suggests beneficial properties
for achieving robust solutions applicable to 5G planning in new areas without
the need of retraining.
