Deep Learning based Coverage and Rate Manifold Estimation in Cellular Networks

Washim Uddin Mondal; Praful D. Mankar; Goutam Das; Vaneet Aggarwal; Satish V. Ukkusuri

doi:10.1109/tccn.2022.3201508

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arXiv · 2202.06390

Deep Learning based Coverage and Rate Manifold Estimation in Cellular Networks

Washim Uddin Mondal·Praful D. Mankar·Goutam Das·Vaneet Aggarwal·Satish V. Ukkusuri

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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.

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Washim Uddin Mondal, Praful D. Mankar, Goutam Das, Vaneet Aggarwal, Satish V. Ukkusuri. 2022-08-21. Deep Learning based Coverage and Rate Manifold Estimation in Cellular Networks. https://doi.org/10.1109/tccn.2022.3201508

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