  In time-varying wireless networks, the states of the communication channels
are subject to random variations, and hence need to be estimated for efficient
rate adaptation and scheduling. The estimation mechanism possesses inaccuracies
that need to be tackled in a probabilistic framework. In this work, we study
scheduling with rate adaptation in single-hop queueing networks under two
levels of channel uncertainty: when the channel estimates are inaccurate but
complete knowledge of the channel/estimator joint statistics is available at
the scheduler; and when the knowledge of the joint statistics is incomplete. In
the former case, we characterize the network stability region and show that a
maximum-weight type scheduling policy is throughput-optimal. In the latter
case, we propose a joint channel statistics learning - scheduling policy. With
an associated trade-off in average packet delay and convergence time, the
proposed policy has a stability region arbitrarily close to the stability
region of the network under full knowledge of channel/estimator joint
statistics.
