Cellular Network Radio Propagation Modeling with Deep Convolutional Neural Networks
This work addresses the need for more accurate and flexible network planning tools for telecom engineers, though it appears incremental as it builds on existing deep learning methods applied to a specific domain.
The authors tackled the problem of inaccurate and inflexible radio propagation modeling for cellular networks by introducing a deep convolutional neural network approach, achieving significantly improved performance over conventional models.
Radio propagation modeling and prediction is fundamental for modern cellular network planning and optimization. Conventional radio propagation models fall into two categories. Empirical models, based on coarse statistics, are simple and computationally efficient, but are inaccurate due to oversimplification. Deterministic models, such as ray tracing based on physical laws of wave propagation, are more accurate and site specific. But they have higher computational complexity and are inflexible to utilize site information other than traditional global information system (GIS) maps. In this article we present a novel method to model radio propagation using deep convolutional neural networks and report significantly improved performance compared to conventional models. We also lay down the framework for data-driven modeling of radio propagation and enable future research to utilize rich and unconventional information of the site, e.g. satellite photos, to provide more accurate and flexible models.