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A Generalized Gaussian Model for Wireless Communications

Abstract : We propose a class of parametric channel models that we call generalized Gaussian model (GGM). In particular, given the input, the output is Gaussian with both mean and covariance depending on the input. More general than the conventional linear model, the GGM can capture nonlinearities and selfinterference present in more and more wireless communication systems. We focus on three key problems. First, we propose a data-driven model identification algorithm that uses training data to fit the underlying channel with a GGM. This is a generalization of the conventional channel estimation procedure. Second, for an identified GGM, we investigate the receiver design problem and propose several decoding metrics. Third, we are interested in the capacity bounds of the GGM. Both the mismatched lower bound and duality upper bound are proposed. Finally, we apply the GGM to fit the multiple-input multiple-output phase-noise channel. Numerical results show the near optimality of the model identification and decoding algorithms.
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Contributor : Khac-Hoang Ngo Connect in order to contact the contributor
Submitted on : Monday, November 8, 2021 - 11:35:17 PM
Last modification on : Monday, November 22, 2021 - 8:32:19 AM
Long-term archiving on: : Wednesday, February 9, 2022 - 7:54:40 PM


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Khac-Hoang Ngo, Sheng Yang. A Generalized Gaussian Model for Wireless Communications. 2021 IEEE International Symposium on Information Theory (ISIT), Jul 2021, Melbourne, Australia. pp.3237-3242, ⟨10.1109/ISIT45174.2021.9517759⟩. ⟨hal-03420087⟩



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