SIAM Review
Lectures on modern convex optimization: analysis, algorithms, and engineering applications
Lectures on modern convex optimization: analysis, algorithms, and engineering applications
Precoding and Signal Shaping for Digital Transmission
Precoding and Signal Shaping for Digital Transmission
Convex Optimization
Wireless Communications
MIMO transceiver design via majorization theory
Foundations and Trends in Communications and Information Theory
Design of Fair Multi-user Transceivers with QoS and Imperfect CSI
CNSR '08 Proceedings of the Communication Networks and Services Research Conference
IEEE Transactions on Signal Processing
SIAM Review
Robust minimum variance beamforming
IEEE Transactions on Signal Processing
Linear precoding via conic optimization for fixed MIMO receivers
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Optimal transmitter eigen-beamforming and space-time block coding based on channel mean feedback
IEEE Transactions on Signal Processing
A competitive minimax approach to robust estimation of random parameters
IEEE Transactions on Signal Processing
Robust MSE equalizer design for MIMO communication systems in the presence of model uncertainties
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Transmit beamforming for physical-layer multicasting
IEEE Transactions on Signal Processing - Part I
Statistically Robust Design of Linear MIMO Transceivers
IEEE Transactions on Signal Processing - Part I
Transmitter optimization and optimality of beamforming for multiple antenna systems
IEEE Transactions on Wireless Communications
Optimization of the MIMO Compound Capacity
IEEE Transactions on Wireless Communications
Combining beamforming and orthogonal space-time block coding
IEEE Transactions on Information Theory
How much training is needed in multiple-antenna wireless links?
IEEE Transactions on Information Theory
MIMO Broadcast Channels With Finite-Rate Feedback
IEEE Transactions on Information Theory
What is the value of limited feedback for MIMO channels?
IEEE Communications Magazine
An introduction to convex optimization for communications and signal processing
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
Tomlinson-Harashima Precoding for Broadcast Channels with Uncertainty
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
On the Design of Linear Transceivers for Multiuser Systems with Channel Uncertainty
IEEE Journal on Selected Areas in Communications
IEEE Transactions on Signal Processing
Fair-rate allocation in multiuser OFDM-SDMA networks
IEEE Transactions on Signal Processing
Robust transceiver optimization in downlink multiuser MIMO systems
IEEE Transactions on Signal Processing
Robust cognitive beamforming with bounded channel uncertainties
IEEE Transactions on Signal Processing
Robust THP transceiver designs for multiuser MIMO downlink with imperfect CSIT
EURASIP Journal on Advances in Signal Processing - Multiuser MIMO Transmission with Limited Feedback, Cooperation, and Coordination
Robust MMSE precoding in MIMO channels with pre-fixed receivers
IEEE Transactions on Signal Processing
On the robustness of transmit beamforming
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Hi-index | 35.71 |
The knowledge of the channel at the transmit side of a communication system can be exploited by using precoding techniques, from which the overall transmission quality might benefit significantly. However, in practical wireless systems, the channel state information is prone to errors, which sometimes deteriorates the performance drastically. In this paper, we address the problem of robust transceiver design in a downlink multiuser system, with respect to the erroneous channel knowledge at the transmitter. The base station is equipped with an antenna array, while users have single antennas. The transceiver optimization is performed under a set of predefined users' quality-of-service constraints, defined as maximum mean square errors, or minimum signal-to-interference-plus-noise ratios (SINRs), which must be satisfied for all disturbances that belong to given, bounded uncertainty sets. Efficient numerical solutious are obtained using semidefinite programming methods from convex optimization theory. The proposed algorithms are found to outperform related approaches in the literature in terms of the achieved performance, while maintaining low computational complexity. The studied uncertainty models are applicable in mitigating typical errors that emerge as a result of quantization or channel estimation.