Joint Estimation of Information and Distributed Link-Scheduling in Wireless Networks: Mean-Field Approximation and Graphical Models

In a large multi-hop wireless network, nodes are preferable to make distributed link-scheduling decisions with information exchange only among a small number of neighbors. However, for a slowly-decaying channel and densely-populated interfering nodes, a small size neighborhood often results in nontrivial link outages and is thus insufficient for making optimal scheduling decisions. A question arises how to incorporate the information outside a neighborhood in distributed link-scheduling. In this work, we develop joint approximation of information and distributed link scheduling. We first apply machine learning approaches to model distributed link-scheduling with complete information. We then characterize the information outside a neighborhood, i.e., the residual interference, as an aggregated random loss variable. The loss variable is characterized by either a Mean-Field approximation or a normal distribution based on the Lyapunov central limit theorem. The approximated information outside a neighborhood is incorporated in a factor graph. This results in joint approximation of information and distributed link-scheduling in an iterative fashion. Link-scheduling decisions are first made at each individual node based on the approximated loss variables. Loss variables are then updated and used for next link-scheduling decisions. The algorithm repeats between these two phases until convergence. Interactive iterations among these variables are implemented by a message-passing algorithm over a factor graph. Simulation results show that using learned information outside a neighborhood jointly with distributed linkscheduling reduces the outage probability close to zero even for a small neighborhood.