Matrix analysis
Optimality of beamforming in fading MIMO multiple access channels
IEEE Transactions on Communications
Joint channel estimation and resource allocation for MIMO systems: part I: single-user analysis
IEEE Transactions on Wireless Communications
Transmit signal design for optimal estimation of correlated MIMO channels
IEEE Transactions on Signal Processing
Transmitter optimization and optimality of beamforming for multiple antenna systems
IEEE Transactions on Wireless Communications
IEEE Transactions on Information Theory
How much training is needed in multiple-antenna wireless links?
IEEE Transactions on Information Theory
Iterative water-filling for Gaussian vector multiple-access channels
IEEE Transactions on Information Theory
Capacity and power allocation for fading MIMO channels with channel estimation error
IEEE Transactions on Information Theory
Optimum Power Allocation for Single-User MIMO and Multi-User MIMO-MAC with Partial CSI
IEEE Journal on Selected Areas in Communications
Joint channel estimation and resource allocation for MIMO systems: part I: single-user analysis
IEEE Transactions on Wireless Communications
Resource Allocation for Multi Access MIMO Systems
International Journal of Mobile Computing and Multimedia Communications
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This is the second part of a two-part paper on the joint channel estimation and resource allocation problem in MIMO systems with noisy channel estimation at the receiver side and partial CSI, in the form of covariance feedback, available at the transmitter side. We consider transmit-side correlated MIMO channels with block fading, where each block is divided into training and data transmission phases. In this paper, we extend the single-user results of Part I to the multiple access channel. For the data transmission phase, we propose an iterative algorithm to solve for the optimum system resources such as time, power and spatial dimensions. This algorithm updates the parameters of the users in a round-robin fashion. In particular, the algorithm updates the training and data transmission parameters of a user, when those of the rest of the users are fixed, in a way to maximize the achievable sum-rate in a multiple access channel; and iterates over users in a round-robin fashion. Finally, we provide a detailed numerical analysis to support the analytical results of both parts of this two-part paper.