Learning to route and schedule links in reconfigurable networks

Abstract

This paper considers networks with a reconfigurable topology with so called 60 GHz dynamic links that can be activated or disabled over time. A fundamental problem is to jointly determine which 60 GHz dynamic links are active and the route chosen by source nodes over time. To this end, this paper outlines a hierarchical deep reinforcement learning solution that can be used to compute the optimal policy that determines for each time slot (i) active dynamic links, and (ii) the route used by each source–destination pair. The results show that the proposed approach results in a maximum average queue length that is 80% shorter than non-learning methods.