Near-perfect Coverage Manifold Estimation in Cellular Networks via conditional GAN
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914673983488000 |
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| author | Mondal, Washim Uddin Goyal, Veni Ukkusuri, Satish V. Das, Goutam Wang, Di Alouini, Mohamed-Slim Aggarwal, Vaneet |
| author_facet | Mondal, Washim Uddin Goyal, Veni Ukkusuri, Satish V. Das, Goutam Wang, Di Alouini, Mohamed-Slim Aggarwal, Vaneet |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06901 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Near-perfect Coverage Manifold Estimation in Cellular Networks via conditional GAN Mondal, Washim Uddin Goyal, Veni Ukkusuri, Satish V. Das, Goutam Wang, Di Alouini, Mohamed-Slim Aggarwal, Vaneet Networking and Internet Architecture 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. |
| title | Near-perfect Coverage Manifold Estimation in Cellular Networks via conditional GAN |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2402.06901 |