This paper presents a conditional generative adversarial network (cGAN) that translates base station location (BSL) information of any Region-of-Interest (RoI) to location-dependent coverage probability values within a subset of that region, called the region-of-evaluation (RoE). We train our network utilizing the BSL data of India, the USA, Germany, and Brazil. In comparison to the state-of-the-art convolutional neural networks (CNNs), our model improves the prediction error (L1 difference between the coverage manifold generated by the network under consideration and that generated via simulation) by two orders of magnitude. Moreover, the cGAN-generated coverage manifolds appear to be almost visually indistinguishable from the ground truth.
Cite
@article{arxiv.2402.06901,
title = {Near-perfect Coverage Manifold Estimation in Cellular Networks via conditional GAN},
author = {Washim Uddin Mondal and Veni Goyal and Satish V. Ukkusuri and Goutam Das and Di Wang and Mohamed-Slim Alouini and Vaneet Aggarwal},
journal= {arXiv preprint arXiv:2402.06901},
year = {2024}
}