A Generalized Gaussian Model for Wireless Communications
Preprint, 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 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.


Khac-Hoang Ngo

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Sheng Yang

University Paris-Saclay

Areas of Advance

Information and Communication Technology

Subject Categories


Control Engineering

Signal Processing

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