We consider multiuser scheduling in wireless networks with
channel variations and flow-level dynamics. Recently, it has
been shown that the MaxWeight algorithm, which is
throughput-optimal in networks with a fixed number users, fails
to achieve the maximum throughput in the presence of flow-level
dynamics. In this talk, we propose a new algorithm, called
\emph{workload-based scheduling with learning}, which is
provably throughput-optimal, requires no prior knowledge of
channels and user demands, and performs significantly better
than previously suggested algorithms.