%0 Journal Article
%T Perturbed Hankel Determinants: Applications to the Information Theory of MIMO Wireless Communications
%A Yang Chen
%A Matthew R. McKay
%J Mathematics
%D 2010
%I arXiv
%X In this paper we compute two important information-theoretic quantities which arise in the application of multiple-input multiple-output (MIMO) antenna wireless communication systems: the distribution of the mutual information of multi-antenna Gaussian channels, and the Gallager random coding upper bound on the error probability achievable by finite-length channel codes. It turns out that the mathematical problem underpinning both quantities is the computation of certain Hankel determinants generated by deformed versions of classical weight functions. For single-user MIMO systems, it is a deformed Laguerre weight, whereas for multi-user MIMO systems it is a deformed Jacobi weight. We apply two different methods to characterize each of these Hankel determinants. First, we employ the ladder operators of the corresponding monic orthogonal polynomials to give an exact characterization of the Hankel determinants in terms of Painlev\'{e} differential equations. This turns out to be a Painlev\'{e} V for the single-user MIMO scenario and a Painlev\'{e} VI for the multi user scenario. We then employ Coulomb fluid methods to derive new closed-form approximations for the Hankel determinants which, although formally valid for large matrix dimensions, are shown to give accurate results for both the MIMO mutual information distribution and the error exponent even when the matrix dimensions are small. Focusing on the single-user mutual information distribution, we then employ both the exact Painlev\'{e} representation and the Coulomb fluid approximation to yield deeper insights into the scaling behavior in terms of the number of antennas and signal-to-noise ratio. Among other things, these results allow us to study the asymptotic Gaussianity of the distribution as the number of antennas increase, and to explicitly compute the correction terms to the mean, variance, and higher order cumulants.
%U http://arxiv.org/abs/1007.0496v1