  In this paper, we endeavour to seek a fundamental understanding of the
potentials and limitations of training-based multiuser multiple-input
multiple-output (MIMO) systems. In a multiuser MIMO system, users are
geographically separated. So, the near-far effect plays an indispensable role
in channel fading. The existing optimal training design for conventional MIMO
does not take the near-far effect into account, and thus is not applicable to a
multiuser MIMO system. In this work, we use the majorization theory as a basic
tool to study the tradeoff between the channel estimation quality and the
information throughput. We establish tight upper and lower bounds of the
throughput, and prove that the derived lower bound is asymptotically optimal
for throughput maximization at high signal-to-noise ratio. Our analysis shows
that the optimal training sequences for throughput maximization in a multiuser
MIMO system are in general not orthogonal to each other. Furthermore, due to
the near-far effect, the optimal training design for throughput maximization is
to deactivate a portion of users with the weakest channels in transmission.
These observations shed light on the practical design of training-based
multiuser MIMO systems.
