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.
@article{arxiv.2603.28121,
title = {Joint Time-Phase Synchronization for Distributed Sensing Networks via Feature-Level Hyper-Plane Regression},
author = {Kailun Tian and Kaili Jiang and Dechang Wang and Yuxin Zhao and Yuxin Shang and Hancong Feng and Bin Tang},
journal= {arXiv preprint arXiv:2603.28121},
year = {2026}
}
Comments
11 pages, 11 figures. This work is under review at the IEEE Internet of Things Journal