Joint Time-Phase Synchronization for Distributed Sensing Networks via Feature-Level Hyper-Plane Regression

TL;DR

This paper introduces a novel hyper-plane regression framework for joint time-phase synchronization in distributed IoT sensing networks, achieving picosecond accuracy with low complexity and minimal communication overhead.

Contribution

It proposes a unified regression model for coupled synchronization parameters, a resource-efficient distributed architecture, and a waveform choice that simplifies computation and communication.

Findings

Achieves picosecond-level synchronization accuracy in noisy, large-scale networks.

Reduces model complexity using linear frequency-modulated waveforms.

Eliminates bidirectional communication overhead with unidirectional feature transmission.

Abstract

Achieving coherent integration in distributed Internet of Things (IoT) sensing networks requires precise synchronization to jointly compensate clock offsets and radio-frequency (RF) phase errors. Conventional two-step protocols suffer from time-phase coupling, where residual timing offsets degrade phase coherence. This paper proposes a generalized hyper-plane regression (GHR) framework for joint calibration by transforming coupled spatiotemporal phase evolution into a unified regression model, enabling effective parameter decoupling. To support resource-constrained IoT edge nodes, a feature-level distributed architecture is developed. By adopting a linear frequency-modulated (LFM) waveform, the model order is reduced, yielding linear computational complexity. In addition, a unidirectional feature transmission mechanism eliminates the communication overhead of bidirectional timestamp exchange, making the approach suitable for resource-constrained IoT networks. Simulation results demonstrate reliable picosecond-level synchronization accuracy under severe noise across kilometer-scale distributed IoT sensing networks.