34 research outputs found
Tight probablisitic MSE constrained multiuser MISO transceiver design under channel uncertainty
A novel optimization method is proposed to solve the probabilistic mean square error (MSE) constrained multiuser multiple-input single-output (MU-MISO) transceiver design problem. Since the probabilistic MSE constraints cannot be expressed in closed-form under Gaussian channel uncertainty, existing probabilistic transceiver design methods rely on probability inequality approximations, resulting in conservative MSE outage realizations. In this paper, based on local structure of the feasible set in the probabilistic MSE constrained transceiver design problem, a set squeezing procedure is proposed to realize tight MSE outage control. Simulation results show that the MSE outage can be realized tightly, which results in significantly reduced transmit power compared to the existing inequality based probabilistic transceiver design.published_or_final_versio
Probabilistic QoS Constrained Robust Downlink Multiuser MIMO Transceiver Design with Arbitrarily Distributed Channel Uncertainty
We study the robust transceiver optimization in downlink multiuser multiple-input multiple-output (MU-MIMO) systems aiming at minimizing transmit power under probabilistic quality-of-service (QoS) requirements. Owing to the unknown distributed interference, the channel estimation error obtained from the linear minimum mean square error (LMMSE) estimator can be arbitrarily distributed. Under this situation, the QoS requirements should account for the worst-case channel estimation error distribution. While directly finding the worst-case distribution is challenging, two methods are proposed to solve the robust transceiver design problem. One is based on the Markov’s inequality, while the other is based on a novel duality method. Two convergence-guaranteed iterative algorithms are proposed to solve the transceiver design problems. Furthermore, for the special case of MU multiple-input single-output (MISO) systems, the corresponding robust transceiver design problems are shown to be convex. Simulation results show that, compared to the non-robust method, the QoS requirement is satisfied by both proposed algorithms. Among the two proposed methods, the duality method shows a superior performance in transmit power, while the Markov method demonstrates a lower computational complexity. Furthermore, the proposed duality method results in less conservative QoS performance than the Gaussian approximated probabilistic robust method and bounded robust method.published_or_final_versio
Tight Probabilistic SINR Constrained Beamforming Under Channel Uncertainties
In downlink multi-user beamforming, a single bases- tation is serving a number of users simultaneously. However, energy intended for one user may leak to other unintended users, causing interference. With signal-to-interference-plus-noise ratio (SINR) being one of the most crucial quality metrics to users, beamforming design with SINR guarantee has always been an important research topic. However, when the channel state information is not accurate, the SINR requirements become probabilistic constraints, which unfortunately are not tractable analytically for general uncertainty distribution. Therefore, ex- isting probabilistic beamforming methods focus on the relatively simple Gaussian and uniform channel uncertainties, and mainly rely on probability inequality based approximated solutions, resulting in conservative SINR outage realizations. In this paper, based on the local structure of the feasible set in the probabilistic beamforming problem, a systematic method is proposed to realize tight SINR outage control for a large class of channel uncertainty distributions. With channel estimation and quantization errors as examples, simulation results show that the SINR outage can be re- alized tightly, which results in reduced transmit power compared to the existing inequality based probabilistic beamformers.published_or_final_versio
Robust Monotonic Optimization Framework for Multicell MISO Systems
The performance of multiuser systems is both difficult to measure fairly and
to optimize. Most resource allocation problems are non-convex and NP-hard, even
under simplifying assumptions such as perfect channel knowledge, homogeneous
channel properties among users, and simple power constraints. We establish a
general optimization framework that systematically solves these problems to
global optimality. The proposed branch-reduce-and-bound (BRB) algorithm handles
general multicell downlink systems with single-antenna users, multiantenna
transmitters, arbitrary quadratic power constraints, and robustness to channel
uncertainty. A robust fairness-profile optimization (RFO) problem is solved at
each iteration, which is a quasi-convex problem and a novel generalization of
max-min fairness. The BRB algorithm is computationally costly, but it shows
better convergence than the previously proposed outer polyblock approximation
algorithm. Our framework is suitable for computing benchmarks in general
multicell systems with or without channel uncertainty. We illustrate this by
deriving and evaluating a zero-forcing solution to the general problem.Comment: Published in IEEE Transactions on Signal Processing, 16 pages, 9
figures, 2 table
Robust Linear Precoder Design for Multi-cell Downlink Transmission
Coordinated information processing by the base stations of multi-cell
wireless networks enhances the overall quality of communication in the network.
Such coordinations for optimizing any desired network-wide quality of service
(QoS) necessitate the base stations to acquire and share some channel state
information (CSI). With perfect knowledge of channel states, the base stations
can adjust their transmissions for achieving a network-wise QoS optimality. In
practice, however, the CSI can be obtained only imperfectly. As a result, due
to the uncertainties involved, the network is not guaranteed to benefit from a
globally optimal QoS. Nevertheless, if the channel estimation perturbations are
confined within bounded regions, the QoS measure will also lie within a bounded
region. Therefore, by exploiting the notion of robustness in the worst-case
sense some worst-case QoS guarantees for the network can be asserted. We adopt
a popular model for noisy channel estimates that assumes that estimation noise
terms lie within known hyper-spheres. We aim to design linear transceivers that
optimize a worst-case QoS measure in downlink transmissions. In particular, we
focus on maximizing the worst-case weighted sum-rate of the network and the
minimum worst-case rate of the network. For obtaining such transceiver designs,
we offer several centralized (fully cooperative) and distributed (limited
cooperation) algorithms which entail different levels of complexity and
information exchange among the base stations.Comment: 38 Pages, 7 Figures, To appear in the IEEE Transactions on Signal
Processin
Transceiver Design for MIMO Systems with Individual Transmit Power Constraints
This paper investigate the transceiver design for single-user multiple-input multipleoutput system (SU-MIMO). Joint transceiver design with an improper modulation is developed based on the minimum total mean-squared error (TMSE) criterion under two different cases. One is equal power allocation (EPA) and other is the power constraint that jointly meets both EPA and total transmit power constraint (TTPC) (i.e ITPC). Transceiver is designed based on the assumption that both the perfect and imperfect channel state information (CSI) is available at both the transmitter and receiver. The simulation results show the performance improvement of the proposed work over conventional work in terms of bit error rate (BER)
Robust Beamforming for Cognitive and Cooperative Wireless Networks
Ph.DDOCTOR OF PHILOSOPH
Robust transmit beamforming design using outage probability specification
Transmit beamforming (precoding) is a powerful technique for enhancing the channel capacity
and reliability of multiple-input and multiple-output (MIMO) wireless systems. The optimum
exploitation of the benefits provided by MIMO systems can be achieved when a perfect channel
state information at transmitter (CSIT) is available. In practices, however, the channel knowledge
is generally imperfect at transmitter because of the inevitable errors induced by finite
feedback channel capacity, quantization and other physical constraints. Such errors degrade the
system performance severely. Hence, robustness has become a crucial issue.
Current robust designs address the channel imperfections with the worst-case and stochastic approaches.
In worst-case analysis, the channel uncertainties are considered as deterministic and
norm-bounded, and the resulting design is a conservative optimization that guarantees a certain
quality of service (QoS) for every allowable perturbation. The latter approach focuses on the
average performance under the assumption of channel statistics, such as mean and covariance.
The system performance could break down when persistent extreme errors occur. Thus, an
outage probability-based approach is developed by keeping a low probability that channel condition
falls below an acceptable level. Compared to the aforementioned methods, this approach
can optimize the average performance as well as consider the extreme scenarios proportionally.
This thesis implements the outage-probability specification into transmit beamforming design
for three scenarios: the single-user MIMO system and the corresponding adaptive modulation
scheme as well as the multi-user MIMO system. In a single-user MIMO system, the transmit
beamformer provides the maximum average received SNR and ensures the robustness to the
CSIT errors by introducing probabilistic constraint on the instantaneous SNR. Beside the robustness
against channel imperfections, the outage probability-based approach also provides a
tight BER bound for adaptive modulation scheme, so that the maximum transmission rate can
be achieved by taking advantage of transmit beamforming. Moreover, in multi-user MIMO
(MU-MIMO) systems, the leakage power is accounted by probability measurement. The resulting
transmit beamformer is designed based on signal-to-leakage-plus-noise ratio (SLNR)
criteria, which maximizes the average received SNR and guarantees the least leakage energy
from the desired user. In such a setting, an outstanding BER performance can be achieved as
well as high reliability of signal-to-interference-plus-noise ratio (SINR).
Given the superior overall performances and significantly improved robustness, the probabilistic
approach provides an attractive alternative to existing robust techniques under imperfect
channel information at transmitter