243 research outputs found
Robust Beamforming for Amplify-and-Forward MIMO Relay Systems Based on Quadratic Matrix Programming
In this paper, robust transceiver design based on minimum-mean-square-error
(MMSE) criterion for dual-hop amplify-and-forward MIMO relay systems is
investigated. The channel estimation errors are modeled as Gaussian random
variables, and then the effect are incorporated into the robust transceiver
based on the Bayesian framework. An iterative algorithm is proposed to jointly
design the precoder at the source, the forward matrix at the relay and the
equalizer at the destination, and the joint design problem can be efficiently
solved by quadratic matrix programming (QMP).Comment: Proceedings of IEEE International Conference on Acoustics, Speech,
and Signal Processing (ICASSP'2010), U.S.
How to Understand LMMSE Transceiver Design for MIMO Systems From Quadratic Matrix Programming
In this paper, a unified linear minimum mean-square-error (LMMSE) transceiver
design framework is investigated, which is suitable for a wide range of
wireless systems. The unified design is based on an elegant and powerful
mathematical programming technology termed as quadratic matrix programming
(QMP). Based on QMP it can be observed that for different wireless systems,
there are certain common characteristics which can be exploited to design LMMSE
transceivers e.g., the quadratic forms. It is also discovered that evolving
from a point-to-point MIMO system to various advanced wireless systems such as
multi-cell coordinated systems, multi-user MIMO systems, MIMO cognitive radio
systems, amplify-and-forward MIMO relaying systems and so on, the quadratic
nature is always kept and the LMMSE transceiver designs can always be carried
out via iteratively solving a number of QMP problems. A comprehensive framework
on how to solve QMP problems is also given. The work presented in this paper is
likely to be the first shoot for the transceiver design for the future
ever-changing wireless systems.Comment: 31 pages, 4 figures, Accepted by IET Communication
Sum-Rate Maximization in Two-Way AF MIMO Relaying: Polynomial Time Solutions to a Class of DC Programming Problems
Sum-rate maximization in two-way amplify-and-forward (AF) multiple-input
multiple-output (MIMO) relaying belongs to the class of difference-of-convex
functions (DC) programming problems. DC programming problems occur as well in
other signal processing applications and are typically solved using different
modifications of the branch-and-bound method. This method, however, does not
have any polynomial time complexity guarantees. In this paper, we show that a
class of DC programming problems, to which the sum-rate maximization in two-way
MIMO relaying belongs, can be solved very efficiently in polynomial time, and
develop two algorithms. The objective function of the problem is represented as
a product of quadratic ratios and parameterized so that its convex part (versus
the concave part) contains only one (or two) optimization variables. One of the
algorithms is called POlynomial-Time DC (POTDC) and is based on semi-definite
programming (SDP) relaxation, linearization, and an iterative search over a
single parameter. The other algorithm is called RAte-maximization via
Generalized EigenvectorS (RAGES) and is based on the generalized eigenvectors
method and an iterative search over two (or one, in its approximate version)
optimization variables. We also derive an upper-bound for the optimal values of
the corresponding optimization problem and show by simulations that this
upper-bound can be achieved by both algorithms. The proposed methods for
maximizing the sum-rate in the two-way AF MIMO relaying system are shown to be
superior to other state-of-the-art algorithms.Comment: 35 pages, 10 figures, Submitted to the IEEE Trans. Signal Processing
in Nov. 201
Joint Transceiver Design Algorithms for Multiuser MISO Relay Systems with Energy Harvesting
In this paper, we investigate a multiuser relay system with simultaneous
wireless information and power transfer. Assuming that both base station (BS)
and relay station (RS) are equipped with multiple antennas, this work studies
the joint transceiver design problem for the BS beamforming vectors, the RS
amplify-and-forward transformation matrix and the power splitting (PS) ratios
at the single-antenna receivers. Firstly, an iterative algorithm based on
alternating optimization (AO) and with guaranteed convergence is proposed to
successively optimize the transceiver coefficients. Secondly, a novel design
scheme based on switched relaying (SR) is proposed that can significantly
reduce the computational complexity and overhead of the AO based designs while
maintaining a similar performance. In the proposed SR scheme, the RS is
equipped with a codebook of permutation matrices. For each permutation matrix,
a latent transceiver is designed which consists of BS beamforming vectors,
optimally scaled RS permutation matrix and receiver PS ratios. For the given
CSI, the optimal transceiver with the lowest total power consumption is
selected for transmission. We propose a concave-convex procedure based and
subgradient-type iterative algorithms for the non-robust and robust latent
transceiver designs. Simulation results are presented to validate the
effectiveness of all the proposed algorithms
Robust MMSE beamforming for multiantenna relay networks
In this paper, we propose a robust minimum mean square error (MMSE) based beamforming
technique for multiantenna relay broadcast channels, where a multi-antenna base station transmits signal to single antenna users with the help of a multiantenna relay. The signal transmission from the base station to the single antenna users is completed in two time slots, where the relay receives the signal from the base station in the first time slot and it then forwards the received signal to different users based on amplify and forward protocol. We propose a robust beamforming technique for sum-power
minimization problem with imperfect channel state information (CSI) between the relay and the users. This robust scheme is developed based on the worst-case optimization framework and Nemirovski
Lemma by incorporating uncertainties in the CSI. The original optimization problem is divided into three subproblems due to joint non-convexity in terms of beamforming vectors at the base station, the relay amplification matrix, and receiver coefficients. These subproblems are formulated into a convex optimization framework by exploiting Nemirovski Lemma, and an iterative algorithm is developed by
alternatively optimizing each of them with channel uncertainties. In addition, we provide an optimization framework to evaluate the achievable worst-case mean square error (MSE) of each user for a given set of design parameters. Simulation results have been provided to validate the convergence of the proposed algorithm
- …