# A Sensing Dataset Protocol for Benchmarking and Multi-Task Wireless Sensing

arXiv: https://arxiv.org/abs/2512.12180 · HTML: https://mlsanity.com/paper/2512.12180/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 13 Dec 2025.

## Builds on (most cited first, 6 of 6)

- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset](https://mlsanity.com/paper/2211.09769/) (2022)
- [M3SC: A Generic Dataset for Mixed Multi-Modal (MMM) Sensing and Communication Integration](https://mlsanity.com/paper/2306.14125/) (2023)
- [Data Augmentation Techniques for Cross-Domain WiFi CSI-based Human Activity Recognition](https://mlsanity.com/paper/2401.00964/) (2024)
- [Device-Free Sensing in OFDM Cellular Network](https://mlsanity.com/paper/2108.09177/) (2021)
- [Sensing as a Service in 6G Perceptive Mobile Networks: Architecture, Advances, and the Road Ahead](https://mlsanity.com/paper/2308.08185/) (2023)

## Abstract (arXiv)

Wireless sensing has become a fundamental enabler for intelligent environments, supporting applications such as human detection, activity recognition, localization, and vital sign monitoring. Despite rapid advances, existing datasets and pipelines remain fragmented across sensing modalities, hindering fair comparison, transfer, and reproducibility. We propose the Sensing Dataset Protocol (SDP), a protocol-level specification and benchmark framework for large-scale wireless sensing. SDP defines how heterogeneous wireless signals are mapped into a unified perception data-block schema through lightweight synchronization, frequency-time alignment, and resampling, while a Canonical Polyadic-Alternating Least Squares (CP-ALS) pooling stage provides a task-agnostic representation that preserves multipath, spectral, and temporal structures. Built upon this protocol, a unified benchmark is established for detection, recognition, and vital-sign estimation with consistent preprocessing, training, and evaluation. Experiments under the cross-user split demonstrate that SDP significantly reduces variance (approximately 88%) across seeds while maintaining competitive accuracy and latency, confirming its value as a reproducible foundation for multi-modal and multitask sensing research.