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Synchronization and Localization in Ad-Hoc ICAS Networks Using a Two-Stage Kuramoto Method

Dominik Neudert-Schulz, Thomas Dallmann

TL;DR

The paper tackles the challenge of achieving joint synchronization and localization in peer-to-peer ICAS vehicular networks where no central controller is available and GNSS-based positioning may be unreliable. It extends the two-stage Kuramoto consensus framework to jointly synchronize frequency, phase, and to estimate LOS propagation delays for mutual localization. The key contributions include a phase-difference decomposition into $\boldsymbol{\varPsi}_{\tau}$ and $\boldsymbol{\varPsi}_{\boldsymbol{\delta}}$, a practical phase-difference estimation scheme with a sampling factor $N_S$, and a drift-compensation mechanism to mitigate finite-sampling effects, validated by simulations. The results demonstrate convergent synchronization and stable, improved localization under realistic sampling conditions, underscoring the approach's potential for robust ICAS in urban, ad-hoc networks.

Abstract

To enable Integrated Communications and Sensing (ICAS) in a peer-to-peer vehicular network, precise synchronization in frequency and phase among the communicating entities is required. In addition, self-driving cars need accurate position estimates of the surrounding vehicles. In this work, we propose a joint, distributed synchronization and localization scheme for a network of communicating entities. Our proposed scheme is mostly signal-agnostic and therefore can be applied to a wide range of possible ICAS signals. We also mitigate the effect of finite sampling frequencies, which otherwise would degrade the synchronization and localization performance severely.

Synchronization and Localization in Ad-Hoc ICAS Networks Using a Two-Stage Kuramoto Method

TL;DR

The paper tackles the challenge of achieving joint synchronization and localization in peer-to-peer ICAS vehicular networks where no central controller is available and GNSS-based positioning may be unreliable. It extends the two-stage Kuramoto consensus framework to jointly synchronize frequency, phase, and to estimate LOS propagation delays for mutual localization. The key contributions include a phase-difference decomposition into and , a practical phase-difference estimation scheme with a sampling factor , and a drift-compensation mechanism to mitigate finite-sampling effects, validated by simulations. The results demonstrate convergent synchronization and stable, improved localization under realistic sampling conditions, underscoring the approach's potential for robust ICAS in urban, ad-hoc networks.

Abstract

To enable Integrated Communications and Sensing (ICAS) in a peer-to-peer vehicular network, precise synchronization in frequency and phase among the communicating entities is required. In addition, self-driving cars need accurate position estimates of the surrounding vehicles. In this work, we propose a joint, distributed synchronization and localization scheme for a network of communicating entities. Our proposed scheme is mostly signal-agnostic and therefore can be applied to a wide range of possible ICAS signals. We also mitigate the effect of finite sampling frequencies, which otherwise would degrade the synchronization and localization performance severely.
Paper Structure (18 sections, 23 equations, 5 figures, 2 tables)

This paper contains 18 sections, 23 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. System model
  3. Signal model
  4. Phases and phase differences
  5. Synchronization and localization
  6. The two-stage Kuramoto method
  7. Estimating transmit times and propagation delays
  8. Proposed algorithm
  9. Notation
  10. Distribution of estimated phase differences
  11. base signal reception
  12. Localization
  13. Synchronization
  14. Simulation
  15. Simulation setup
  16. ...and 3 more sections

Figures (5)

  • Figure 1: Exemplary transmit signal $\mathrm{x}_{p}\!\left(t\right)$ of some host $p$ consisting of several base signals $\mathrm{s}_{p,n}\!\left(t\right)$
  • Figure 2: Exemplary scenario as seen form host $q$: Host $q$ receives base signals from host $p$ and transmits its own base signals. Vertical lines indicate the point in time of transmission (red/circle) and reception (blue/square), respectively.
  • Figure 3: Scenario A: Simulation without sampling error
  • Figure 4: Scenario B: Simulation with sampling error and without drift compensation
  • Figure 5: Scenario C: Simulation with sampling error and drift compensation