Near-perfect Coverage Manifold Estimation in Cellular Networks via conditional GAN
<|reference_start|>Near-perfect Coverage Manifold Estimation in Cellular Networks via conditional GAN: 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 ($L_1$ 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.<|reference_end|>
@article{mondal2024near-perfect,
    title={Near-perfect Coverage Manifold Estimation in Cellular Networks via
  conditional GAN},
    author={Washim Uddin Mondal, Veni Goyal, Satish V. Ukkusuri, Goutam Das, Di
  Wang, Mohamed-Slim Alouini, Vaneet Aggarwal},
    journal={IEEE Networking Letters, 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06901},
    primaryClass={cs.NI}
}