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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.28121 |
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| _version_ | 1866917368656035840 |
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| author | Tian, Kailun Jiang, Kaili Wang, Dechang Zhao, Yuxin Shang, Yuxin Feng, Hancong Tang, Bin |
| author_facet | Tian, Kailun Jiang, Kaili Wang, Dechang Zhao, Yuxin Shang, Yuxin Feng, Hancong Tang, Bin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28121 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Joint Time-Phase Synchronization for Distributed Sensing Networks via Feature-Level Hyper-Plane Regression Tian, Kailun Jiang, Kaili Wang, Dechang Zhao, Yuxin Shang, Yuxin Feng, Hancong Tang, Bin Signal Processing 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. |
| title | Joint Time-Phase Synchronization for Distributed Sensing Networks via Feature-Level Hyper-Plane Regression |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2603.28121 |