A neural approach to synchronization in wireless networks with heterogeneous sources of noise

TL;DR

This paper introduces a neural-based method for clock synchronization in wireless networks that effectively handles non-stationary noise sources like temperature variations and delay asymmetry without prior environmental assumptions.

Contribution

It proposes a robust, offline-specified neural approach to dynamic state estimation for synchronization, eliminating the need for parameter tuning based on environmental conditions.

Findings

Robust to temperature variations

Effective under different delay distributions

Handles asymmetry in transmission paths

Abstract

The paper addresses state estimation for clock synchronization in the presence of factors affecting the quality of synchronization. Examples are temperature variations and delay asymmetry. These working conditions make synchronization a challenging problem in many wireless environments, such as Wireless Sensor Networks or WiFi. Dynamic state estimation is investigated as it is essential to overcome non-stationary noises. The two-way timing message exchange synchronization protocol has been taken as a reference. No a-priori assumptions are made on the stochastic environments and no temperature measurement is executed. The algorithms are unequivocally specified offline, without the need of tuning some parameters in dependence of the working conditions. The presented approach reveals to be robust to a large set of temperature variations, different delay distributions and levels of asymmetry in the transmission path.