Cooperative Synchronization in Wireless Networks

TL;DR

This paper introduces Bayesian inference methods for fully distributed phase and skew synchronization in wireless networks, analyzing their convergence and performance against existing algorithms and bounds.

Contribution

It develops belief propagation and mean field message passing algorithms for synchronization, applicable with or without master nodes, and compares their effectiveness with Bayesian bounds.

Findings

Both methods achieve effective synchronization in simulations.

Algorithms outperform some existing methods in convergence speed.

Performance approaches Bayesian Cramér-Rao bounds.

Abstract

Synchronization is a key functionality in wireless network, enabling a wide variety of services. We consider a Bayesian inference framework whereby network nodes can achieve phase and skew synchronization in a fully distributed way. In particular, under the assumption of Gaussian measurement noise, we derive two message passing methods (belief propagation and mean field), analyze their convergence behavior, and perform a qualitative and quantitative comparison with a number of competing algorithms. We also show that both methods can be applied in networks with and without master nodes. Our performance results are complemented by, and compared with, the relevant Bayesian Cram\'er-Rao bounds.