1,366 research outputs found
On the Outage Capacity of Correlated Multiple-Path MIMO Channels
The use of multi-antenna arrays in both transmission and reception has been
shown to dramatically increase the throughput of wireless communication
systems. As a result there has been considerable interest in characterizing the
ergodic average of the mutual information for realistic correlated channels.
Here, an approach is presented that provides analytic expressions not only for
the average, but also the higher cumulant moments of the distribution of the
mutual information for zero-mean Gaussian (multiple-input multiple-output) MIMO
channels with the most general multipath covariance matrices when the channel
is known at the receiver. These channels include multi-tap delay paths, as well
as general channels with covariance matrices that cannot be written as a
Kronecker product, such as dual-polarized antenna arrays with general
correlations at both transmitter and receiver ends. The mathematical methods
are formally valid for large antenna numbers, in which limit it is shown that
all higher cumulant moments of the distribution, other than the first two scale
to zero. Thus, it is confirmed that the distribution of the mutual information
tends to a Gaussian, which enables one to calculate the outage capacity. These
results are quite accurate even in the case of a few antennas, which makes this
approach applicable to realistic situations.Comment: submitted for publication IEEE Trans. Information Theory; IEEEtran
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Eigenvalue Dynamics of a Central Wishart Matrix with Application to MIMO Systems
We investigate the dynamic behavior of the stationary random process defined
by a central complex Wishart (CW) matrix as it varies along a
certain dimension . We characterize the second-order joint cdf of the
largest eigenvalue, and the second-order joint cdf of the smallest eigenvalue
of this matrix. We show that both cdfs can be expressed in exact closed-form in
terms of a finite number of well-known special functions in the context of
communication theory. As a direct application, we investigate the dynamic
behavior of the parallel channels associated with multiple-input
multiple-output (MIMO) systems in the presence of Rayleigh fading. Studying the
complex random matrix that defines the MIMO channel, we characterize the
second-order joint cdf of the signal-to-noise ratio (SNR) for the best and
worst channels. We use these results to study the rate of change of MIMO
parallel channels, using different performance metrics. For a given value of
the MIMO channel correlation coefficient, we observe how the SNR associated
with the best parallel channel changes slower than the SNR of the worst
channel. This different dynamic behavior is much more appreciable when the
number of transmit () and receive () antennas is similar. However, as
is increased while keeping fixed, we see how the best and worst
channels tend to have a similar rate of change.Comment: 15 pages, 9 figures and 1 table. This work has been accepted for
publication at IEEE Trans. Inf. Theory. Copyright (c) 2014 IEEE. Personal use
of this material is permitted. However, permission to use this material for
any other purposes must be obtained from the IEEE by sending a request to
[email protected]
On the Distribution of MIMO Mutual Information: An In-Depth Painlev\'{e} Based Characterization
This paper builds upon our recent work which computed the moment generating
function of the MIMO mutual information exactly in terms of a Painlev\'{e} V
differential equation. By exploiting this key analytical tool, we provide an
in-depth characterization of the mutual information distribution for
sufficiently large (but finite) antenna numbers. In particular, we derive
systematic closed-form expansions for the high order cumulants. These results
yield considerable new insight, such as providing a technical explanation as to
why the well known Gaussian approximation is quite robust to large SNR for the
case of unequal antenna arrays, whilst it deviates strongly for equal antenna
arrays. In addition, by drawing upon our high order cumulant expansions, we
employ the Edgeworth expansion technique to propose a refined Gaussian
approximation which is shown to give a very accurate closed-form
characterization of the mutual information distribution, both around the mean
and for moderate deviations into the tails (where the Gaussian approximation
fails remarkably). For stronger deviations where the Edgeworth expansion
becomes unwieldy, we employ the saddle point method and asymptotic integration
tools to establish new analytical characterizations which are shown to be very
simple and accurate. Based on these results we also recover key well
established properties of the tail distribution, including the
diversity-multiplexing-tradeoff.Comment: Submitted to IEEE Transaction on Information Theory (under revision
Precoded Integer-Forcing Universally Achieves the MIMO Capacity to Within a Constant Gap
An open-loop single-user multiple-input multiple-output communication scheme
is considered where a transmitter, equipped with multiple antennas, encodes the
data into independent streams all taken from the same linear code. The coded
streams are then linearly precoded using the encoding matrix of a perfect
linear dispersion space-time code. At the receiver side, integer-forcing
equalization is applied, followed by standard single-stream decoding. It is
shown that this communication architecture achieves the capacity of any
Gaussian multiple-input multiple-output channel up to a gap that depends only
on the number of transmit antennas.Comment: to appear in the IEEE Transactions on Information Theor
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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