488 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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A Bayesian Framework for Collaborative Multi-Source Signal Detection
This paper introduces a Bayesian framework to detect multiple signals
embedded in noisy observations from a sensor array. For various states of
knowledge on the communication channel and the noise at the receiving sensors,
a marginalization procedure based on recent tools of finite random matrix
theory, in conjunction with the maximum entropy principle, is used to compute
the hypothesis selection criterion. Quite remarkably, explicit expressions for
the Bayesian detector are derived which enable to decide on the presence of
signal sources in a noisy wireless environment. The proposed Bayesian detector
is shown to outperform the classical power detector when the noise power is
known and provides very good performance for limited knowledge on the noise
power. Simulations corroborate the theoretical results and quantify the gain
achieved using the proposed Bayesian framework.Comment: 15 pages, 9 pictures, Submitted to IEEE Trans. on Signal Processin
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]
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