  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 paper, we propose a new algorithm, called 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.
