Washim Uddin Mondal;Veni Goyal;Satish V. Ukkusuri;Goutam Das;Di Wang;Mohamed-Slim Alouini;Vaneet Aggarwal
{"title":"通过条件 GAN 在蜂窝网络中实现近乎完美的覆盖态势估计","authors":"Washim Uddin Mondal;Veni Goyal;Satish V. Ukkusuri;Goutam Das;Di Wang;Mohamed-Slim Alouini;Vaneet Aggarwal","doi":"10.1109/LNET.2024.3365717","DOIUrl":null,"url":null,"abstract":"This letter 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 (\n<inline-formula> <tex-math>$L_{1}$ </tex-math></inline-formula>\n 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.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 2","pages":"97-100"},"PeriodicalIF":0.0000,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Near-Perfect Coverage Manifold Estimation in Cellular Networks via Conditional GAN\",\"authors\":\"Washim Uddin Mondal;Veni Goyal;Satish V. Ukkusuri;Goutam Das;Di Wang;Mohamed-Slim Alouini;Vaneet Aggarwal\",\"doi\":\"10.1109/LNET.2024.3365717\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This letter 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 (\\n<inline-formula> <tex-math>$L_{1}$ </tex-math></inline-formula>\\n 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.\",\"PeriodicalId\":100628,\"journal\":{\"name\":\"IEEE Networking Letters\",\"volume\":\"6 2\",\"pages\":\"97-100\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Networking Letters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10433712/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Networking Letters","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10433712/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Near-Perfect Coverage Manifold Estimation in Cellular Networks via Conditional GAN
This letter 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.