1,295 research outputs found
Structure of Optimal Input Covariance Matrices for MIMO Systems with Covariance Feedback under General Correlated Fading
We describe the structure of optimal Input covariance matrices for single
user multiple-input/multiple-output (MIMO) communication system with covariance
feedback and for general correlated fading. Our approach is based on the novel
concept of right commutant and recovers previously derived results for the
Kronecker product models. Conditions are derived which allow a significant
simplification of the optimization problem.Comment: 5 pages, corrected typo
Optimal Transmit Covariance for Ergodic MIMO Channels
In this paper we consider the computation of channel capacity for ergodic
multiple-input multiple-output channels with additive white Gaussian noise. Two
scenarios are considered. Firstly, a time-varying channel is considered in
which both the transmitter and the receiver have knowledge of the channel
realization. The optimal transmission strategy is water-filling over space and
time. It is shown that this may be achieved in a causal, indeed instantaneous
fashion. In the second scenario, only the receiver has perfect knowledge of the
channel realization, while the transmitter has knowledge of the channel gain
probability law. In this case we determine an optimality condition on the input
covariance for ergodic Gaussian vector channels with arbitrary channel
distribution under the condition that the channel gains are independent of the
transmit signal. Using this optimality condition, we find an iterative
algorithm for numerical computation of optimal input covariance matrices.
Applications to correlated Rayleigh and Ricean channels are given.Comment: 22 pages, 14 figures, Submitted to IEEE Transactions on Information
Theor
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
Fundamental Limits in Correlated Fading MIMO Broadcast Channels: Benefits of Transmit Correlation Diversity
We investigate asymptotic capacity limits of the Gaussian MIMO broadcast
channel (BC) with spatially correlated fading to understand when and how much
transmit correlation helps the capacity. By imposing a structure on channel
covariances (equivalently, transmit correlations at the transmitter side) of
users, also referred to as \emph{transmit correlation diversity}, the impact of
transmit correlation on the power gain of MIMO BCs is characterized in several
regimes of system parameters, with a particular interest in the large-scale
array (or massive MIMO) regime. Taking the cost for downlink training into
account, we provide asymptotic capacity bounds of multiuser MIMO downlink
systems to see how transmit correlation diversity affects the system
multiplexing gain. We make use of the notion of joint spatial division and
multiplexing (JSDM) to derive the capacity bounds. It is advocated in this
paper that transmit correlation diversity may be of use to significantly
increase multiplexing gain as well as power gain in multiuser MIMO systems. In
particular, the new type of diversity in wireless communications is shown to
improve the system multiplexing gain up to by a factor of the number of degrees
of such diversity. Finally, performance limits of conventional large-scale MIMO
systems not exploiting transmit correlation are also characterized.Comment: 29 pages, 8 figure
A Generalized Framework on Beamformer Design and CSI Acquisition for Single-Carrier Massive MIMO Systems in Millimeter Wave Channels
In this paper, we establish a general framework on the reduced dimensional
channel state information (CSI) estimation and pre-beamformer design for
frequency-selective massive multiple-input multiple-output MIMO systems
employing single-carrier (SC) modulation in time division duplex (TDD) mode by
exploiting the joint angle-delay domain channel sparsity in millimeter (mm)
wave frequencies. First, based on a generic subspace projection taking the
joint angle-delay power profile and user-grouping into account, the reduced
rank minimum mean square error (RR-MMSE) instantaneous CSI estimator is derived
for spatially correlated wideband MIMO channels. Second, the statistical
pre-beamformer design is considered for frequency-selective SC massive MIMO
channels. We examine the dimension reduction problem and subspace (beamspace)
construction on which the RR-MMSE estimation can be realized as accurately as
possible. Finally, a spatio-temporal domain correlator type reduced rank
channel estimator, as an approximation of the RR-MMSE estimate, is obtained by
carrying out least square (LS) estimation in a proper reduced dimensional
beamspace. It is observed that the proposed techniques show remarkable
robustness to the pilot interference (or contamination) with a significant
reduction in pilot overhead
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