Low-latency routing for fronthaul network: A Monte Carlo machine learning approach

Abstract

A fronthaul bridged network has attracted attention as a way of efficiently constructing the centralized radio access network (C-RAN) architecture. If we change the functional split of C-RAN and employ time-division duplex (TDD), the data rate in fronthaul will become variable and the global synchronization of fronthaul streams will occur. This feature results in an increase in the queuing delay in fronthaul bridges among fronthaul flows. This paper proposes a novel low-latency routing scheme designed to satisfy the latency requirements in fronthaul networks with path-control protocols. The proposed scheme formulates the maximum queuing delay by defining competitive links and flows. It selects the set of paths that satisfy the latency requirements with the Markov chain Monte Carlo method using machine learning (MCMC-ML). The initial paths are selected from candidate paths using the learned solutions, and path-reselection is performed with the MCMC method. We confirmed with computer simulations that the proposed scheme can compute routes for all flows that satisfy the delay requirements. We also confirmed that the route computation is accelerated with the learned solutions, even if the flow distribution changes.