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.

翻译：我们解决了在多跳无线网络中无需链路速率先验知识的学习与调度联合问题。以往的调度算法需要链路速率信息，而学习算法通常需要集中式实体且具有多项式复杂度。这些成为开发大规模多跳网络中基于学习的高效分布式资源分配方案的主要障碍。本文通过融合学习算法，在数据包到达动态变化且无链路速率先验知识的情况下，开发了具有可证明效率的调度方案。我们将结果扩展至分布式实现，并通过仿真评估其性能。