ITALYSIG: Open and High Fidelity I/Q Signal Database With Tutorial and Applications for Wireless Research
Bibliographic record
Abstract
High-fidelity in-phase and quadrature (I/Q) signal traces are critical for a variety of wireless network applications, including spectrum monitoring, interference detection and mitigation, radio-frequency (RF) fingerprinting (RFFP), smart jamming detection, anomaly identification, and modulation classification. However, the number and scope of publicly available I/Q datasets are currently limited, as most datasets are either restricted to a single frequency or wireless technology, or collected in controlled laboratory environments. This paper introduces ITALYSIG, a comprehensive, high-definition, open-source I/Q database of diverse real-world radio signals, including cellular, radar, and other wireless technologies. ITALYSIG provides I/Q captures with up to 100 MHz bandwidth, collected across diverse urban and rural environments in Italy. The I/Q signals are stored in multiple formats, including raw binary files, standardized VITA Radio Transport (VRT, VITA-49), and visual formats such as Portable Network Graphics (PNG) and JavaScript Object Notation (JSON) for broader applicability. The data-acquisition setup is based on a CRFS RFeye SenS Portable recorder at the front end, which enables automatic long-term I/Q recordings on the order of hours, multi-terabyte storage, and real-time signal processing. In addition to releasing the dataset, this article provides a comprehensive overview and qualitative comparison of state-of-the-art datasets in terms of measurement setup, data format, wireless technology, frequencies, and time duration. Furthermore, this article provides a tutorial on the end-to-end measurement setup for automatic I/Q acquisition, multi-format export, and back-end storage, as well as real-time analysis via the DeepView software. Finally, we provide guidelines for integrating I/Q traces into deep learning pipelines and highlight specific use cases of the dataset.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".