Bayesian Joint Synchronization and Localization Based on Asymmetric Time-stamp Exchange

TL;DR

This paper introduces a Bayesian method for joint synchronization and localization of mobile nodes in dense networks, utilizing asymmetric timestamp exchange, AoA estimation, and recursive filtering to achieve high accuracy in position and clock offset.

Contribution

It presents a novel Bayesian recursive filtering approach that combines timestamp exchange, AoA, and delay information for joint synchronization and localization.

Findings

Position RMSE below 1 meter

Clock offset RMSE below 1 nanosecond

Effective in ultra dense network scenarios

Abstract

In this work, we study the joint synchronization and localization (sync&loc) of Mobile Nodes (MNs) in ultra dense networks. In particular, we deploy an asymmetric timestamp exchange mechanism between MNs and Access Nodes (ANs), that, traditionally, provides us with information about the MNs' clock offset and skew. However, information about the distance between an AN and a MN is also intrinsic to the propagation delay experienced by exchanged time-stamps. In addition, we utilize Angle of Arrival (AoA) estimation to determine the incoming direction of time-stamp exchange packets, which gives further information about the MNs' location. Finally, we employ Bayesian Recursive Filtering (BRF) to combine the aforementioned pieces of information and jointly estimate the position and clock parameters of MNs. The simulation results indicate that the Root Mean Square Errors (RMSEs) of position and clock offset estimation are kept below 1 meter and 1 ns, respectively.