2,710 research outputs found
Why Does a Kronecker Model Result in Misleading Capacity Estimates?
Many recent works that study the performance of multi-input multi-output
(MIMO) systems in practice assume a Kronecker model where the variances of the
channel entries, upon decomposition on to the transmit and the receive
eigen-bases, admit a separable form. Measurement campaigns, however, show that
the Kronecker model results in poor estimates for capacity. Motivated by these
observations, a channel model that does not impose a separable structure has
been recently proposed and shown to fit the capacity of measured channels
better. In this work, we show that this recently proposed modeling framework
can be viewed as a natural consequence of channel decomposition on to its
canonical coordinates, the transmit and/or the receive eigen-bases. Using tools
from random matrix theory, we then establish the theoretical basis behind the
Kronecker mismatch at the low- and the high-SNR extremes: 1) Sparsity of the
dominant statistical degrees of freedom (DoF) in the true channel at the
low-SNR extreme, and 2) Non-regularity of the sparsity structure (disparities
in the distribution of the DoF across the rows and the columns) at the high-SNR
extreme.Comment: 39 pages, 5 figures, under review with IEEE Trans. Inform. Theor
Kronecker Sum Decompositions of Space-Time Data
In this paper we consider the use of the space vs. time Kronecker product
decomposition in the estimation of covariance matrices for spatio-temporal
data. This decomposition imposes lower dimensional structure on the estimated
covariance matrix, thus reducing the number of samples required for estimation.
To allow a smooth tradeoff between the reduction in the number of parameters
(to reduce estimation variance) and the accuracy of the covariance
approximation (affecting estimation bias), we introduce a diagonally loaded
modification of the sum of kronecker products representation [1]. We derive a
Cramer-Rao bound (CRB) on the minimum attainable mean squared predictor
coefficient estimation error for unbiased estimators of Kronecker structured
covariance matrices. We illustrate the accuracy of the diagonally loaded
Kronecker sum decomposition by applying it to video data of human activity.Comment: 5 pages, 8 figures, accepted to CAMSAP 201
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