219 research outputs found
Asymptotic Analysis of Double-Scattering Channels
We consider a multiple-input multiple-output (MIMO) multiple access channel
(MAC), where the channel between each transmitter and the receiver is modeled
by the doubly-scattering channel model. Based on novel techniques from random
matrix theory, we derive deterministic approximations of the mutual
information, the signal-to-noise-plus-interference-ratio (SINR) at the output
of the minimum-mean-square-error (MMSE) detector and the sum-rate with MMSE
detection which are almost surely tight in the large system limit. Moreover, we
derive the asymptotically optimal transmit covariance matrices. Our simulation
results show that the asymptotic analysis provides very close approximations
for realistic system dimensions.Comment: 5 pages, 2 figures, submitted to the Annual Asilomar Conference on
Signals, Systems, and Computers, Pacific Grove, CA, USA, 201
A Differential Feedback Scheme Exploiting the Temporal and Spectral Correlation
Channel state information (CSI) provided by limited feedback channel can be
utilized to increase the system throughput. However, in multiple input multiple
output (MIMO) systems, the signaling overhead realizing this CSI feedback can
be quite large, while the capacity of the uplink feedback channel is typically
limited. Hence, it is crucial to reduce the amount of feedback bits. Prior work
on limited feedback compression commonly adopted the block fading channel model
where only temporal or spectral correlation in wireless channel is considered.
In this paper, we propose a differential feedback scheme with full use of the
temporal and spectral correlations to reduce the feedback load. Then, the
minimal differential feedback rate over MIMO doubly selective fading channel is
investigated. Finally, the analysis is verified by simulations
Random Beamforming over Correlated Fading Channels
We study a multiple-input multiple-output (MIMO) multiple access channel
(MAC) from several multi-antenna transmitters to a multi-antenna receiver. The
fading channels between the transmitters and the receiver are modeled by random
matrices, composed of independent column vectors with zero mean and different
covariance matrices. Each transmitter is assumed to send multiple data streams
with a random precoding matrix extracted from a Haar-distributed matrix. For
this general channel model, we derive deterministic approximations of the
normalized mutual information, the normalized sum-rate with
minimum-mean-square-error (MMSE) detection and the
signal-to-interference-plus-noise-ratio (SINR) of the MMSE decoder, which
become arbitrarily tight as all system parameters grow infinitely large at the
same speed. In addition, we derive the asymptotically optimal power allocation
under individual or sum-power constraints. Our results allow us to tackle the
problem of optimal stream control in interference channels which would be
intractable in any finite setting. Numerical results corroborate our analysis
and verify its accuracy for realistic system dimensions. Moreover, the
techniques applied in this paper constitute a novel contribution to the field
of large random matrix theory and could be used to study even more involved
channel models.Comment: 35 pages, 5 figure
MIMO Networks: the Effects of Interference
Multiple-input/multiple-output (MIMO) systems promise enormous capacity
increase and are being considered as one of the key technologies for future
wireless networks. However, the decrease in capacity due to the presence of
interferers in MIMO networks is not well understood. In this paper, we develop
an analytical framework to characterize the capacity of MIMO communication
systems in the presence of multiple MIMO co-channel interferers and noise. We
consider the situation in which transmitters have no information about the
channel and all links undergo Rayleigh fading. We first generalize the known
determinant representation of hypergeometric functions with matrix arguments to
the case when the argument matrices have eigenvalues of arbitrary multiplicity.
This enables the derivation of the distribution of the eigenvalues of Gaussian
quadratic forms and Wishart matrices with arbitrary correlation, with
application to both single user and multiuser MIMO systems. In particular, we
derive the ergodic mutual information for MIMO systems in the presence of
multiple MIMO interferers. Our analysis is valid for any number of interferers,
each with arbitrary number of antennas having possibly unequal power levels.
This framework, therefore, accommodates the study of distributed MIMO systems
and accounts for different positions of the MIMO interferers.Comment: Submitted to IEEE Trans. on Info. Theor
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
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