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.

翻译：新一代移动网络的部署面临诸多严峻挑战，其影响因素包括成本效益权衡、现有基础设施以及新技术特性等。其中，5G部署的主要难题之一在于如何在兼顾多样化业务性能指标的同时实现最优5G无线覆盖。本文提出一种基于深度学习的方法，通过利用前代蜂窝网络数据辅助5G无线规划。该方案采用定制的图表示方法以充分利用现有蜂窝网络的可用信息，并运用图神经网络（GNN）模型高效处理此类数据。在评估中，我们使用英国某移动网络运营商的真实数据，测试了该方法建模从4G向5G非独立组网（NSA）过渡的潜力。实验结果表明，该方案在预测新建5G蜂窝网络关键性能指标方面具有高精度：当对训练区域内的样本进行评估时，平均绝对百分比误差（MAPE）小于17%。此外，我们进一步测试了该方案在未参与训练的多个地理区域上的泛化能力，其MAPE小于19%。这表明该方法具备构建鲁棒解决方案的优良特性，可无需重新训练即可应用于新区域的5G规划。