Joint Approximation of Information and Distributed Link-Scheduling Decisions in Wireless Networks

TL;DR

This paper introduces a novel joint approximation method combining machine learning and probabilistic modeling to improve distributed link-scheduling in wireless networks, significantly reducing outages even with limited neighborhood information.

Contribution

It develops a joint approximation framework that models external interference using residual loss variables and integrates it into distributed scheduling via message passing.

Findings

Reduces link outage probability close to zero with small neighborhoods

Effectively models external interference using residual loss variables

Iterative message passing improves scheduling accuracy

Abstract

For a large multi-hop wireless network, nodes are preferable to make distributed and localized link-scheduling decisions with only interactions among a small number of neighbors. However, for a slowly decaying channel and densely populated interferers, a small size neighborhood often results in nontrivial link outages and is thus insufficient for making optimal scheduling decisions. A question arises how to deal with 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 in form of residual interference as a random loss variable. The loss variable is further 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 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 with a message-passing algorithm over a factor graph. Simulation results show that using learned information outside a neighborhood jointly with distributed link-scheduling reduces the outage probability close to zero even for a small neighborhood.