Matrix analysis
SIAM Review
Determinant Maximization with Linear Matrix Inequality Constraints
SIAM Journal on Matrix Analysis and Applications
MIMO transceiver design via majorization theory
Foundations and Trends in Communications and Information Theory
Transceiver optimization for block-based multiple access through ISI channels
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Transmitter Optimization for the Multi-Antenna Downlink With Per-Antenna Power Constraints
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Journal on Selected Areas in Communications
Joint MMSE transceiver designs and performance benchmark for CoMP transmission and reception
ISRN Communications and Networking
Optimal joint design of orthonormal real valued short time block code and linear transceiver
Digital Signal Processing
Hi-index | 35.68 |
This correspondence revisits the joint transceiver optimization problem for multiple-input multiple-output (MIMO) channels. The linear transceiver as well as the transceiver with linear precoding and decision feedback equalization are considered. For both types of transceivers, in addition to the usual total power constraint, an individual power constraint on each antenna element is also imposed. A number of objective functions including the average bit error rate, are considered for both of the above systems under the generalized power constraint. It is shown that for both types of systems the optimization problem can be solved by first solving a class of MMSE problems (AM-MMSE or GM-MMSE depending on the type of transceiver), and then using majorization theory. The first step,under the generalized power constraint, can be formulated as a semidefinite program (SDP) for both types of transceivers, and can be solved efficiently by convex optimization tools. The second step is addressed by using results from majorization theory. The framework developed here is general enough to add any finite number of linear constraints to the covariance matrix of the input.