DNN-assisted Particle-based Bayesian Joint Synchronization and Localization

TL;DR

This paper introduces DePF, a deep neural network-assisted particle filter method for joint synchronization and localization in dense networks, improving accuracy over existing algorithms by integrating time-stamp exchange, CIR analysis, and Bayesian filtering.

Contribution

The paper presents a novel hybrid particle-Gaussian mixture Bayesian filter that jointly estimates user position and clock parameters using deep learning and advanced signal processing techniques.

Findings

Achieves sub-meter position estimation accuracy in 90% of cases.

Reduces clock offset estimation error below 2 nanoseconds in most scenarios.

Outperforms state-of-the-art joint sync&loc algorithms like EKF and linearized BRF.

Abstract

In this work, we propose a Deep neural network-assisted Particle Filter-based (DePF) approach to address the Mobile User (MU) joint synchronization and localization (sync\&loc) problem in ultra dense networks. In particular, DePF deploys an asymmetric time-stamp exchange mechanism between the MUs and the Access Points (APs), which, traditionally, provides us with information about the MUs' clock offset and skew. However, information about the distance between an AP and an MU is also intrinsic to the propagation delay experienced by exchanged time-stamps. In addition, to estimate the angle of arrival of the received synchronization packet, DePF draws on the multiple signal classification algorithm that is fed by Channel Impulse Response (CIR) experienced by the sync packets. The CIR is also leveraged on to determine the link condition, i.e. Line-of-Sight (LoS) or Non-LoS. Finally, to perform joint sync\&loc, DePF capitalizes on particle Gaussian mixtures that allow for a hybrid particle-based and parametric Bayesian Recursive Filtering (BRF) fusion of the aforementioned pieces of information and thus jointly estimate the position and clock parameters of the MUs. The simulation results verifies the superiority of the proposed algorithm over the state-of-the-art schemes, especially that of Extended Kalman filter- and linearized BRF-based joint sync\&loc. In particular, only drawing on the synchronization time-stamp exchange and CIRs, for 90$\%$of the cases, the absolute position and clock offset estimation error remain below 1 meter and 2 nanoseconds, respectively.