Deep Reinforcement Learning for Scheduling in Multi-Hop Wireless Networks : Invited Paper

The efficient scheduling of transmission links in a wireless network with a certain optimization objective and subject to the interference and network flow constraints plays a central role in wireless networking research. As an alternative to traditional mathematical analysis, data-driven learning methods have shown promise in solving difficult problems by extracting knowledge from experiences and inspired applications of machine learning in wireless networking. In this paper, we focus on tackling the fundamental scheduling issue in multi-hop wireless networks with machine learning, facing the great challenges of the involvement of non-differentiable operations and the consideration of variable network topologies. To address these issues, we propose a reinforcement learning-based method to solve a class of network flow problems under the protocol interference model. Learning from experience, the proposed approach develops a strategy to sequentially select optimum subsets of links to transmit simultaneously to maximize the system throughput without causing interference. The model structure is designed in a way that incorporates network topological information to allow a flexible number of network nodes, and allows non-differentiable decision operation to pass informative gradient information. Experiments with synthetic and real-world deployment data demonstrate that the proposed algorithm achieves close-to-optimum performance at a significantly reduced time cost.