Multiuser transmit beamforming for MIMO uplink with individual SINR targets
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Weighted sum rate maximization in the MIMO MAC with linear transceivers: asymptotic results
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Efficient weighted sum rate maximization with linear precoding
IEEE Transactions on Signal Processing
On downlink beamforming with greedy user selection: performance analysis and a simple new algorithm
IEEE Transactions on Signal Processing - Part I
Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels
IEEE Transactions on Signal Processing
User Selection With Zero-Forcing Beamforming Achieves the Asymptotically Optimal Sum Rate
IEEE Transactions on Signal Processing - Part I
Iterative water-filling for Gaussian vector multiple-access channels
IEEE Transactions on Information Theory
On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming
IEEE Journal on Selected Areas in Communications
Weighted sum rate maximization in the MIMO MAC with linear transceivers: asymptotic results
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
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The problem of maximizing weighted sum rate in the MIMO multiple access channel with individual power constraints is considered. The optimum is achieved by successive interference cancellation, where the covariances are found by iterative water-filling. As successive interference cancellation implies long decoding delays, we consider linear approaches with zero-forcing constraints. To avoid the associated non-convex and combinatorial optimization, we allocate successively data streams to users, while keeping transmit filters and user allocations of previous steps fixed. The transmit filters are determined based on two lower bounds for the weighted sum rate. The algorithms converge to the optimum linear solution for infinite transmit powers in many scenarios at low computational complexity.