A Generalized Gaussian Model for Wireless Communications
Paper i proceeding, 2021

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 conventionallinear model, the GGM can capture nonlinearities and self-interference 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 detection 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 detection algorithms.


Khac-Hoang Ngo

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Sheng Yang

Université Paris-Saclay

IEEE International Symposium on Information Theory - Proceedings

21578095 (ISSN)

Vol. 2021-July 3237-3242
9781538682098 (ISBN)

2021 IEEE International Symposium on Information Theory, ISIT 2021
Virtual, Melbourne, Australia,


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