CSMA using the Bethe Approximation: Scheduling and Utility Maximization

TL;DR

This paper introduces a distributed algorithm for CSMA that uses the Bethe approximation to achieve polynomial-time convergence for scheduling and utility maximization, outperforming traditional MCMC-based methods.

Contribution

It develops a novel distributed iterative algorithm based on the Bethe approximation, enabling fast convergence with only one iteration for stability and utility maximization.

Findings

Algorithm converges in polynomial time.

Requires only one iteration for stability.

Produces highly accurate solutions and converges faster.

Abstract

CSMA (Carrier Sense Multiple Access), which resolves contentions over wireless networks in a fully distributed fashion, has recently gained a lot of attentions since it has been proved that appropriate control of CSMA parameters guarantees optimality in terms of stability (i.e., scheduling) and system- wide utility (i.e., scheduling and congestion control). Most CSMA-based algorithms rely on the popular MCMC (Markov Chain Monte Carlo) technique, which enables one to find optimal CSMA parameters through iterative loops of `simulation-and-update'. However, such a simulation-based approach often becomes a major cause of exponentially slow convergence, being poorly adaptive to flow/topology changes. In this paper, we develop distributed iterative algorithms which produce approximate solutions with convergence in polynomial time for both stability and utility maximization problems. In particular, for the stability problem, the proposed distributed algorithm requires, somewhat surprisingly, only one iteration among links. Our approach is motivated by the Bethe approximation (introduced by Yedidia, Freeman and Weiss in 2005) allowing us to express approximate solutions via a certain non-linear system with polynomial size. Our polynomial convergence guarantee comes from directly solving the non-linear system in a distributed manner, rather than multiple simulation-and-update loops in existing algorithms. We provide numerical results to show that the algorithm produces highly accurate solutions and converges much faster than the prior ones.