Published April 29, 2026 | Version v1.0.0
Dataset Open

0cal Artifact Dataset: Raw Probe Measurements for mmWave Calibration in Line-of-Sight and Non-Line-of-Sight Environments

Authors/Creators

  • 1. Florida Statte University

Contributors

Contact person:

Description

This dataset contains the raw probe measurements used in the SenSys 2026 paper “0cal: Zero-Cost Calibration for mmWave Networks” (DOI: 10.1145/3774906.3802748).

The dataset includes raw probe measurements collected in both Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) environments for evaluating mmWave antenna-array calibration. These raw probe files are the primary non-derivable experimental inputs. Other intermediate results, such as phase-deviation outputs, steering-vector results, and plotting artifacts, can be regenerated from the associated MATLAB code.

Contents:
- Line-Of-Sight/instance_probe/: raw probe measurements in LoS settings
- None-Line-Of-Sight/instance_probe/: raw probe measurements in NLoS settings

The associated code, plotting scripts, and reproduction instructions are maintained in the project GitHub repository:
xinliulab/26SenSys-0cal: Code for 0cal

This dataset supplements the published paper and is intended for artifact evaluation and reproducibility. A typical workflow is:
1. Download the raw probe archives from this record.
2. Clone the GitHub repository containing the MATLAB scripts.
3. Run the preprocessing, calibration, and plotting scripts to regenerate the derived results and paper figures.
4. Follow the repository README for the figure-to-script mapping and environment details.

Files

Files (30.0 GB)

Name Size Download all
md5:4f10219843d85cb6d521075473dd6276
15.0 GB Download
md5:ad9360d968bb619af9a109754ca575a4
15.0 GB Download

Additional details

Related works

Is supplement to
Dataset: 10.1145/3774906.3802748 (DOI)

Funding

U.S. National Science Foundation
AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE) 2112471
U.S. National Science Foundation
ECCS-2128567

Software

Programming language
MATLAB