Elements of information theory
Elements of information theory
Linear precoding via conic optimization for fixed MIMO receivers
IEEE Transactions on Signal Processing
Rate Optimization for Multiuser MIMO Systems With Linear Processing
IEEE Transactions on Signal Processing - Part II
Transmitter Optimization for the Multi-Antenna Downlink With Per-Antenna Power Constraints
IEEE Transactions on Signal Processing
Iterative multiuser uplink and downlink beamforming under SINR constraints
IEEE Transactions on Signal Processing
Sum capacity of Gaussian vector broadcast channels
IEEE Transactions on Information Theory
Mutual information and minimum mean-square error in Gaussian channels
IEEE Transactions on Information Theory
Sum power iterative water-filling for multi-antenna Gaussian broadcast channels
IEEE Transactions on Information Theory
Gradient of mutual information in linear vector Gaussian channels
IEEE Transactions on Information Theory
The Capacity Region of the Gaussian Multiple-Input Multiple-Output Broadcast Channel
IEEE Transactions on Information Theory
On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming
IEEE Journal on Selected Areas in Communications
An iterative water-filling algorithm for maximum weighted sum-rate of Gaussian MIMO-BC
IEEE Journal on Selected Areas in Communications
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This paper studies linear transmit filter design for Weighted Sum-Rate (WSR) maximization in the Multiple Input Multiple Output Broadcast Channel (MIMO-BC). The problem of finding the optimal transmit filter is non-convex and intractable to solve using low complexity methods. Motivated by recent results highlighting the relationship between mutual information and Minimum Mean Square Error (MMSE), this paper establishes a relationship between weighted sum-rate and weighted MMSE in the MIMO-BC. The relationship is used to propose a low complexity algorithm for finding a local weighted sum-rate optimum based on alternating optimization. Numerical results studying sum-rate show that the proposed algorithm achieves high performance with few iterations.