Deep Learning based Coverage and Rate Manifold Estimation in Cellular Networks
This work addresses network planning for cellular operators by enabling more accurate performance estimation, though it is incremental as it applies deep learning to an existing domain-specific task.
The paper tackles the problem of predicting location-dependent coverage and rate in cellular networks from topology, achieving a 40% reduction in coverage prediction error and a 25% reduction in rate prediction error compared to stochastic geometry models.
This article proposes Convolutional Neural Network-based Auto Encoder (CNN-AE) to predict location-dependent rate and coverage probability of a network from its topology. We train the CNN utilising BS location data of India, Brazil, Germany, and the USA and compare its performance with stochastic geometry (SG) based analytical models. In comparison to the best-fitted SG-based model, CNN-AE improves the coverage and rate prediction errors by a margin of as large as $40\%$ and $25\%$ respectively. As an application, we propose a low complexity, provably convergent algorithm that, using trained CNN-AE, can compute locations of new BSs that need to be deployed in a network in order to satisfy pre-defined spatially heterogeneous performance goals.