Deep Learning based Coverage and Rate Manifold Estimation in Cellular Networks

TL;DR

This paper introduces a CNN-AE model for predicting coverage and rate in cellular networks, demonstrating significant accuracy improvements over traditional stochastic geometry models and enabling efficient network planning.

Contribution

The paper presents a novel CNN-AE approach for location-dependent network performance prediction and a low-complexity algorithm for optimal base station deployment.

Findings

CNN-AE reduces prediction errors by up to 40% for coverage and 25% for rate.

Model trained on data from multiple countries shows strong generalization.

Proposed deployment algorithm effectively meets performance goals.

Abstract

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.