3,063 research outputs found
Communication Over MIMO Broadcast Channels Using Lattice-Basis Reduction
A simple scheme for communication over MIMO broadcast channels is introduced
which adopts the lattice reduction technique to improve the naive channel
inversion method. Lattice basis reduction helps us to reduce the average
transmitted energy by modifying the region which includes the constellation
points. Simulation results show that the proposed scheme performs well, and as
compared to the more complex methods (such as the perturbation method) has a
negligible loss. Moreover, the proposed method is extended to the case of
different rates for different users. The asymptotic behavior of the symbol
error rate of the proposed method and the perturbation technique, and also the
outage probability for the case of fixed-rate users is analyzed. It is shown
that the proposed method, based on LLL lattice reduction, achieves the optimum
asymptotic slope of symbol-error-rate (called the precoding diversity). Also,
the outage probability for the case of fixed sum-rate is analyzed.Comment: Submitted to IEEE Trans. on Info. Theory (Jan. 15, 2006), Revised
(Jun. 12, 2007
Eigen-Based Transceivers for the MIMO Broadcast Channel with Semi-Orthogonal User Selection
This paper studies the sum rate performance of two low complexity
eigenmode-based transmission techniques for the MIMO broadcast channel,
employing greedy semi-orthogonal user selection (SUS). The first approach,
termed ZFDPC-SUS, is based on zero-forcing dirty paper coding; the second
approach, termed ZFBF-SUS, is based on zero-forcing beamforming. We first
employ new analytical methods to prove that as the number of users K grows
large, the ZFDPC-SUS approach can achieve the optimal sum rate scaling of the
MIMO broadcast channel. We also prove that the average sum rates of both
techniques converge to the average sum capacity of the MIMO broadcast channel
for large K. In addition to the asymptotic analysis, we investigate the sum
rates achieved by ZFDPC-SUS and ZFBF-SUS for finite K, and show that ZFDPC-SUS
has significant performance advantages. Our results also provide key insights
into the benefit of multiple receive antennas, and the effect of the SUS
algorithm. In particular, we show that whilst multiple receive antennas only
improves the asymptotic sum rate scaling via the second-order behavior of the
multi-user diversity gain; for finite K, the benefit can be very significant.
We also show the interesting result that the semi-orthogonality constraint
imposed by SUS, whilst facilitating a very low complexity user selection
procedure, asymptotically does not reduce the multi-user diversity gain in
either first (log K) or second-order (loglog K) terms.Comment: 35 pages, 3 figures, to appear in IEEE transactions on signal
processin
Sum Rates, Rate Allocation, and User Scheduling for Multi-User MIMO Vector Perturbation Precoding
This paper considers the multiuser multiple-input multiple-output (MIMO)
broadcast channel. We consider the case where the multiple transmit antennas
are used to deliver independent data streams to multiple users via vector
perturbation. We derive expressions for the sum rate in terms of the average
energy of the precoded vector, and use this to derive a high signal-to-noise
ratio (SNR) closed-form upper bound, which we show to be tight via simulation.
We also propose a modification to vector perturbation where different rates can
be allocated to different users. We conclude that for vector perturbation
precoding most of the sum rate gains can be achieved by reducing the rate
allocation problem to the user selection problem. We then propose a
low-complexity user selection algorithm that attempts to maximize the high-SNR
sum rate upper bound. Simulations show that the algorithm outperforms other
user selection algorithms of similar complexity.Comment: 27 pages with 6 figures and 2 tables. Accepted for publication in
IEEE Trans. Wireless Comm
Artificial Noise-Aided Biobjective Transmitter Optimization for Service Integration in Multi-User MIMO Gaussian Broadcast Channel
This paper considers an artificial noise (AN)-aided transmit design for
multi-user MIMO systems with integrated services. Specifically, two sorts of
service messages are combined and served simultaneously: one multicast message
intended for all receivers and one confidential message intended for only one
receiver and required to be perfectly secure from other unauthorized receivers.
Our interest lies in the joint design of input covariances of the multicast
message, confidential message and artificial noise (AN), such that the
achievable secrecy rate and multicast rate are simultaneously maximized. This
problem is identified as a secrecy rate region maximization (SRRM) problem in
the context of physical-layer service integration. Since this bi-objective
optimization problem is inherently complex to solve, we put forward two
different scalarization methods to convert it into a scalar optimization
problem. First, we propose to prefix the multicast rate as a constant, and
accordingly, the primal biobjective problem is converted into a secrecy rate
maximization (SRM) problem with quality of multicast service (QoMS) constraint.
By varying the constant, we can obtain different Pareto optimal points. The
resulting SRM problem can be iteratively solved via a provably convergent
difference-of-concave (DC) algorithm. In the second method, we aim to maximize
the weighted sum of the secrecy rate and the multicast rate. Through varying
the weighted vector, one can also obtain different Pareto optimal points. We
show that this weighted sum rate maximization (WSRM) problem can be recast into
a primal decomposable form, which is amenable to alternating optimization (AO).
Then we compare these two scalarization methods in terms of their overall
performance and computational complexity via theoretical analysis as well as
numerical simulation, based on which new insights can be drawn.Comment: 14 pages, 5 figure
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