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In this paper, we consider a time-slotted MIMO point-to-multipoint network, which, for example, could represent either a cellular downlink, or a cluster in a wireless mesh network. The transmitter sends an independent data stream from each transmit antenna to communicate simultaneously to a subset of the receivers in each slot. The transmitter sends training symbols at the beginning of each slot so that each receiver can estimate the channel and determine the data rate it could potentially receive from each transmit antenna after configuring its antenna weights appropriately. For this purpose, we develop a maximum-likelihood estimator of the signal to interference plus noise ratio (SINR) from the received training symbols. The transmitter decides which receivers to serve in each slot to maximize the minimum normalized average data rate realized by each receiver. This scheduling decision is made based on estimated SINR information fed back by receivers. Through some numerical examples, we discuss some general issues with regard to exploitation of multi-user diversity in the context of imperfect channel state information. For example, the scheduler introduces a bias for positive SINR estimation errors, which is important to take into account when the receivers declare a potential data rate in each slot. Index Terms--Multiuser diversity, stochastic approximation, maximum-likelihood estimation, cross-layer design.

For any multiple-input multiple-output (MIMO) network with linear beamformers, it is shown that there exists a dual network that attains the same signal-to-interference-plus-noise ratio (SINR) performance. This network duality concept is a generalization of the virtual uplink concept investigated by Rashid-Farrokhi et al.. We first develop the dual relation using a generic duality theory in linear programming. This approach naturally leads to the construction of a dual network, and provides machinery for handling a generalized cost function. We then consider the joint MIMO beamforming and power control problem with individual SINR constraints. We apply the network duality to this problem and propose a high-performance iterative algorithm which, compared with past centralized approaches, has improved convergence behavior. Finally, we propose a simpler distributed version of the algorithm tailored for a cellular downlink. This algorithm can be readily implemented in a distributed fashion, since it does not require information exchange between base stations. The algorithm achieves performance close to that of centralized ones, and outperforms other decentralized approaches available in the literature. Index Terms--Beamforming, duality, linear program, multiple-input multiple-output (MIMO), power control.