  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.
