Vector quantization and signal compression
Vector quantization and signal compression
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Robust Tomlinson–Harashima Precoding for the Wireless Broadcast Channel
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Linear transmit processing in MIMO communications systems
IEEE Transactions on Signal Processing - Part I
Time-Variant Channel Estimation Using Discrete Prolate Spheroidal Sequences
IEEE Transactions on Signal Processing
Iterative multiuser uplink and downlink beamforming under SINR constraints
IEEE Transactions on Signal Processing
New transmit schemes and simplified receivers for MIMO wireless communication systems
IEEE Transactions on Wireless Communications
Precoding in multiantenna and multiuser communications
IEEE Transactions on Wireless Communications
Efficient feedback methods for MIMO channels based on parameterization
IEEE Transactions on Wireless Communications
Sum capacity of the vector Gaussian broadcast channel and uplink-downlink duality
IEEE Transactions on Information Theory
What is the value of limited feedback for MIMO channels?
IEEE Communications Magazine
IEEE Journal on Selected Areas in Communications
Vectored transmission for digital subscriber line systems
IEEE Journal on Selected Areas in Communications
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We consider the robust precoder design for multiuser multiple-input single-output (MU-MISO) systems where the channel state information (CSI) is fed back from the single antenna receivers to the centralized transmitter equipped with multiple antennas. We propose to compress the feedback data by projecting the channel estimates onto a vector basis, known at the receivers and the transmitter, and quantizing the resulting coefficients. The channel estimator and the basis for the rank reduction are jointly optimized by minimizing the mean-square error (MSE) between the true and the rank-reduced CSI. Expressions for the conditional mean and the conditional covariance of the channel are derived which are necessary for the robust precoder design. These expressions take into account the following sources of error: channel estimation, truncation for rank reduction, quantization, and feedback channel delay. As an example for the robust problem formulation, vector pre coding (VP) is designed based on the expectation of the MSE conditioned on the fed-back CSI. Our results show that robust precoding based on fed-back CSI clearly outperforms conventional precoding designs which do not take into account the errors in the CSI.