Scheduling with Rate Adaptation under Imperfect Channel-Estimator Joint Statistics

In time varying wireless networks, the state of 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 the statistics of the mismatch is known and when the mismatch statistics is unknown. In the former case, we characterize the network stability region and show that a back-pressure type scheduling policy is throughput optimal. For the latter case, we propose a joint channel learning and scheduling policy and show that it has a stability region that can be arbitrarily close to the network stability region with an associated trade-off in the average packet delay.