dorsal/arxiv
View SchemaSDP: A Unified Protocol and Benchmarking Framework for Reproducible Wireless Sensing
| Authors | Di Zhang, Jiawei Huang, Yuanhao Cui, Xiaowen Cao, Tony Xiao Han, Xiaojun Jing, Christos Masouros |
|---|---|
| Categories | |
| ArXiv ID | 2601.08463vv1 |
| URL | https://arxiv.org/abs/2601.08463 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
Learning-based wireless sensing has made rapid progress, yet the field still lacks a unified and reproducible experimental foundation. Unlike computer vision, wireless sensing relies on hardware-dependent channel measurements whose representations, preprocessing pipelines, and evaluation protocols vary significantly across devices and datasets, hindering fair comparison and reproducibility. This paper proposes the Sensing Data Protocol (SDP), a protocol-level abstraction and unified benchmark for scalable wireless sensing. SDP acts as a standardization layer that decouples learning tasks from hardware heterogeneity. To this end, SDP enforces deterministic physical-layer sanitization, canonical tensor construction, and standardized training and evaluation procedures, decoupling learning performance from hardware-specific artifacts. Rather than introducing task-specific models, SDP establishes a principled protocol foundation for fair evaluation across diverse sensing tasks and platforms. Extensive experiments demonstrate that SDP achieves competitive accuracy while substantially improving stability, reducing inter-seed performance variance by orders of magnitude on complex activity recognition tasks. A real-world experiment using commercial off-the-shelf Wi-Fi hardware further illustrating the protocol's interoperability across heterogeneous hardware. By providing a unified protocol and benchmark, SDP enables reproducible and comparable wireless sensing research and supports the transition from ad hoc experimentation toward reliable engineering practice.
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"abstract": "Learning-based wireless sensing has made rapid progress, yet the field still lacks a unified and reproducible experimental foundation. Unlike computer vision, wireless sensing relies on hardware-dependent channel measurements whose representations, preprocessing pipelines, and evaluation protocols vary significantly across devices and datasets, hindering fair comparison and reproducibility.\n This paper proposes the Sensing Data Protocol (SDP), a protocol-level abstraction and unified benchmark for scalable wireless sensing. SDP acts as a standardization layer that decouples learning tasks from hardware heterogeneity. To this end, SDP enforces deterministic physical-layer sanitization, canonical tensor construction, and standardized training and evaluation procedures, decoupling learning performance from hardware-specific artifacts. Rather than introducing task-specific models, SDP establishes a principled protocol foundation for fair evaluation across diverse sensing tasks and platforms. Extensive experiments demonstrate that SDP achieves competitive accuracy while substantially improving stability, reducing inter-seed performance variance by orders of magnitude on complex activity recognition tasks. A real-world experiment using commercial off-the-shelf Wi-Fi hardware further illustrating the protocol\u0027s interoperability across heterogeneous hardware. By providing a unified protocol and benchmark, SDP enables reproducible and comparable wireless sensing research and supports the transition from ad hoc experimentation toward reliable engineering practice.",
"arxiv_id": "2601.08463",
"authors": [
"Di Zhang",
"Jiawei Huang",
"Yuanhao Cui",
"Xiaowen Cao",
"Tony Xiao Han",
"Xiaojun Jing",
"Christos Masouros"
],
"categories": [
"eess.SP"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "SDP: A Unified Protocol and Benchmarking Framework for Reproducible Wireless Sensing",
"url": "https://arxiv.org/abs/2601.08463",
"version": "v1"
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