<p dir="ltr">This research introduces an approach utilizing a novel conditional generative adversarial network (cGAN) tailored specifically for the prediction of cellular network coverage. In comparison to state-of-the-art method convolutional neural networks (CNNs), our cGAN model offers a significant improvement by translating base station locations within any Region-of-Interest (RoI) into precise coverage probability values within a designated region-of-evaluation (RoE). </p><p dir="ltr">By leveraging base station location data from diverse geographical and infrastructural landscapes spanning regions like India, the USA, Germany, and Brazil, our model demonstrates superior predictive performance compared to existing CNN-based approaches. Notably, the prediction error, as quantified by the L1 norm, is reduced by two orders of magnitude in comparison to state-of-the-art CNN models.</p><p dir="ltr">Furthermore, the coverage manifolds generated by our cGAN model closely resemble those produced by conventional simulation methods, indicating a substantial advancement in both prediction accuracy and visual fidelity. This achievement underscores the potential of cGANs in enhancing the precision and reliability of cellular network performance prediction, offering promising implications for optimizing network planning and deployment strategies.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/25711050 |
| Date | 29 April 2024 |
| Creators | Veni Goyal (18457590) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Coverage_Manifold_Estimation_in_Cellular_Networks_via_Conditional_GANs/25711050 |
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