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1	Coverage Manifold Estimation in Cellular Networks via Conditional GANs Veni Goyal (18457590) 29 April 2024 (has links) <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> Industrial engineering Networking and communications Deep learning
2	Probabilistic Regression using Conditional Generative Adversarial Networks Oskarsson, Joel January 2020 (has links) Regression is a central problem in statistics and machine learning with applications everywhere in science and technology. In probabilistic regression the relationship between a set of features and a real-valued target variable is modelled as a conditional probability distribution. There are cases where this distribution is very complex and not properly captured by simple approximations, such as assuming a normal distribution. This thesis investigates how conditional Generative Adversarial Networks (GANs) can be used to properly capture more complex conditional distributions. GANs have seen great success in generating complex high-dimensional data, but less work has been done on their use for regression problems. This thesis presents experiments to better understand how conditional GANs can be used in probabilistic regression. Different versions of GANs are extended to the conditional case and evaluated on synthetic and real datasets. It is shown that conditional GANs can learn to estimate a wide range of different distributions and be competitive with existing probabilistic regression models. machine learning ml regression probabilistic distribution cgan gan conditional gan adversarial networks neural network deep learning f-gan f-cgan f-divergence adversarial training bimodal heteroskedastic mmd maximum mean discrepancy gmmn generative moment matching network conditional gmmn ipm kde cgan evaluation cgan regression gan regression cgan-regression regression using gan deep nn implicit generative conditional model complex noise aleatoric uncertainty dctd mdn heteroskedastic regression gp Other Computer and Information Science Annan data- och informationsvetenskap

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Coverage Manifold Estimation in Cellular Networks via Conditional GANs

Probabilistic Regression using Conditional Generative Adversarial Networks