2,700 research outputs found
A Deterministic Equivalent for the Analysis of Non-Gaussian Correlated MIMO Multiple Access Channels
Large dimensional random matrix theory (RMT) has provided an efficient
analytical tool to understand multiple-input multiple-output (MIMO) channels
and to aid the design of MIMO wireless communication systems. However, previous
studies based on large dimensional RMT rely on the assumption that the transmit
correlation matrix is diagonal or the propagation channel matrix is Gaussian.
There is an increasing interest in the channels where the transmit correlation
matrices are generally nonnegative definite and the channel entries are
non-Gaussian. This class of channel models appears in several applications in
MIMO multiple access systems, such as small cell networks (SCNs). To address
these problems, we use the generalized Lindeberg principle to show that the
Stieltjes transforms of this class of random matrices with Gaussian or
non-Gaussian independent entries coincide in the large dimensional regime. This
result permits to derive the deterministic equivalents (e.g., the Stieltjes
transform and the ergodic mutual information) for non-Gaussian MIMO channels
from the known results developed for Gaussian MIMO channels, and is of great
importance in characterizing the spectral efficiency of SCNs.Comment: This paper is the revision of the original manuscript titled "A
Deterministic Equivalent for the Analysis of Small Cell Networks". We have
revised the original manuscript and reworked on the organization to improve
the presentation as well as readabilit
Iterative Deterministic Equivalents for the Performance Analysis of Communication Systems
In this article, we introduce iterative deterministic equivalents as a novel
technique for the performance analysis of communication systems whose channels
are modeled by complex combinations of independent random matrices. This
technique extends the deterministic equivalent approach for the study of
functionals of large random matrices to a broader class of random matrix models
which naturally arise as channel models in wireless communications. We present
two specific applications: First, we consider a multi-hop amplify-and-forward
(AF) MIMO relay channel with noise at each stage and derive deterministic
approximations of the mutual information after the Kth hop. Second, we study a
MIMO multiple access channel (MAC) where the channel between each transmitter
and the receiver is represented by the double-scattering channel model. We
provide deterministic approximations of the mutual information, the
signal-to-interference-plus-noise ratio (SINR) and sum-rate with
minimum-mean-square-error (MMSE) detection and derive the asymptotically
optimal precoding matrices. In both scenarios, the approximations can be
computed by simple and provably converging fixed-point algorithms and are shown
to be almost surely tight in the limit when the number of antennas at each node
grows infinitely large. Simulations suggest that the approximations are
accurate for realistic system dimensions. The technique of iterative
deterministic equivalents can be easily extended to other channel models of
interest and is, therefore, also a new contribution to the field of random
matrix theory.Comment: submitted to the IEEE Transactions on Information Theory, 43 pages, 4
figure
Linear MIMO Precoding in Jointly-Correlated Fading Multiple Access Channels with Finite Alphabet Signaling
In this paper, we investigate the design of linear precoders for
multiple-input multiple-output (MIMO) multiple access channels (MAC). We assume
that statistical channel state information (CSI) is available at the
transmitters and consider the problem under the practical finite alphabet input
assumption. First, we derive an asymptotic (in the large-system limit) weighted
sum rate (WSR) expression for the MIMO MAC with finite alphabet inputs and
general jointly-correlated fading. Subsequently, we obtain necessary conditions
for linear precoders maximizing the asymptotic WSR and propose an iterative
algorithm for determining the precoders of all users. In the proposed
algorithm, the search space of each user for designing the precoding matrices
is its own modulation set. This significantly reduces the dimension of the
search space for finding the precoding matrices of all users compared to the
conventional precoding design for the MIMO MAC with finite alphabet inputs,
where the search space is the combination of the modulation sets of all users.
As a result, the proposed algorithm decreases the computational complexity for
MIMO MAC precoding design with finite alphabet inputs by several orders of
magnitude. Simulation results for finite alphabet signalling indicate that the
proposed iterative algorithm achieves significant performance gains over
existing precoder designs, including the precoder design based on the Gaussian
input assumption, in terms of both the sum rate and the coded bit error rate.Comment: 7 pages, 2 figures, accepted for ICC1
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