  We address the joint problem of learning and scheduling in multi-hop wireless
network without a prior knowledge on link rates. Previous scheduling algorithms
need the link rate information, and learning algorithms often require a
centralized entity and polynomial complexity. These become a major obstacle to
develop an efficient learning-based distributed scheme for resource allocation
in large-scale multi-hop networks. In this work, by incorporating with learning
algorithm, we develop provably efficient scheduling scheme under packet arrival
dynamics without a priori link rate information. We extend the results to
distributed implementation and evaluation their performance through
simulations.
