24 research outputs found
Random Beamforming over Quasi-Static and Fading Channels: A Deterministic Equivalent Approach
In this work, we study the performance of random isometric precoders over
quasi-static and correlated fading channels. We derive deterministic
approximations of the mutual information and the
signal-to-interference-plus-noise ratio (SINR) at the output of the
minimum-mean-square-error (MMSE) receiver and provide simple provably
converging fixed-point algorithms for their computation. Although these
approximations are only proven exact in the asymptotic regime with infinitely
many antennas at the transmitters and receivers, simulations suggest that they
closely match the performance of small-dimensional systems. We exemplarily
apply our results to the performance analysis of multi-cellular communication
systems, multiple-input multiple-output multiple-access channels (MIMO-MAC),
and MIMO interference channels. The mathematical analysis is based on the
Stieltjes transform method. This enables the derivation of deterministic
equivalents of functionals of large-dimensional random matrices. In contrast to
previous works, our analysis does not rely on arguments from free probability
theory which enables the consideration of random matrix models for which
asymptotic freeness does not hold. Thus, the results of this work are also a
novel contribution to the field of random matrix theory and applicable to a
wide spectrum of practical systems.Comment: to appear in IEEE Transactions on Information Theory, 201
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
Performance Analysis of Massive MIMO Networks with Random Unitary Pilot Matrices
A common approach to obtain channel state information for massive MIMO
networks is to use the same orthogonal training sequences in each cell. We call
this the full-pilot reuse (FPR) scheme. In this paper, we study an alternative
approach where each cell uses different sets of orthogonal pilot (DOP)
sequences. Considering uplink communications with matched filter (MF)
receivers, we first derive the SINR in the large system regime where the number
of antennas at the base station, the number of users in each cell, and training
duration grow large with fixed ratios. For tractability in the analysis, the
orthogonal pilots are drawn from Haar distributed random unitary matrices. The
resulting expression is simple and easy to compute. As shown by the numerical
simulations, the asymptotic SINR approximates the finite-size systems
accurately. Secondly, we derive the user capacity of the DOP scheme under a
simple power control and show that it is generally better than that of the FPR
scheme.Comment: Draf
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
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
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
Linear precoding for multicarrier and multicast PLC
International audienceOne of the first publications of its kind in the exciting field of multiple input multiple output (MIMO) power line communications (PLC), MIMO Power Line Communications: Narrow and Broadband Standards, EMC, and Advanced Processing contains contributions from experts in industry and academia, making it practical enough to provide a solid understanding of how PLC technologies work, yet scientific enough to form a base for ongoing R&D activities. This book is subdivided into five thematic parts. Part I looks at narrow- and broadband channel characterization based on measurements from around the globe. Taking into account current regulations and electromagnetic compatibility (EMC), part II describes MIMO signal processing strategies and related capacity and throughput estimates. Current narrow- and broadband PLC standards and specifications are described in the various chapters of part III. Advanced PLC processing options are treated in part IV, drawing from a wide variety of research areas such as beamforming/precoding, time reversal, multi-user processing, and relaying. Lastly, part V contains case studies and field trials, where the advanced technologies of tomorrow are put into practice today. Suitable as a reference or a handbook, MIMO Power Line Communications: Narrow and Broadband Standards, EMC, and Advanced Processing features self-contained chapters with extensive cross-referencing to allow for a flexible reading path