# WiSig: A Large-Scale WiFi Signal Dataset for Receiver and Channel Agnostic RF Fingerprinting

arXiv: https://arxiv.org/abs/2112.15363 · HTML: https://mlsanity.com/paper/2112.15363/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2021.

## Built on by (most cited first, 21 of 21)

- [Mitigating Receiver Impact on Radio Frequency Fingerprint Identification via Domain Adaptation](https://mlsanity.com/paper/2404.08566/) (2024)
- [Joint Signal Detection and Automatic Modulation Classification via Deep Learning](https://mlsanity.com/paper/2405.00736/) (2024)
- [Edge AI-based Radio Frequency Fingerprinting for IoT Networks](https://mlsanity.com/paper/2412.10553/) (2024)
- [EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding](https://mlsanity.com/paper/2508.18785/) (2025)
- [Radio Frequency Fingerprinting via Deep Learning: Challenges and Opportunities](https://mlsanity.com/paper/2310.16406/) (2023)
- [Revolution of Wireless Signal Recognition for 6G: Recent Advances, Challenges and Future Directions](https://mlsanity.com/paper/2503.08091/) (2025)
- [DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning](https://mlsanity.com/paper/2507.12011/) (2025)
- [Bluetooth and WiFi Dataset for Real World RF Fingerprinting of Commercial Devices](https://mlsanity.com/paper/2303.13538/) (2023)
- [Cross-Receiver Generalization for RF Fingerprint Identification via Feature Disentanglement and Adversarial Training](https://mlsanity.com/paper/2510.09405/) (2025)
- [Exploiting Radio Frequency Fingerprints for Device Identification: Tackling Cross-receiver Challenges in the Source-data-free Scenario](https://mlsanity.com/paper/2512.16648/) (2025)
- [SignCRF: Scalable Channel-agnostic Data-driven Radio Authentication System](https://mlsanity.com/paper/2303.12811/) (2023)
- [MobRFFI: Non-cooperative Device Re-identification for Mobility Intelligence](https://mlsanity.com/paper/2503.02156/) (2025)
- [Protocol-agnostic and Data-free Backdoor Attacks on Pre-trained Models in RF Fingerprinting](https://mlsanity.com/paper/2505.00881/) (2025)
- [HyDRA: A Hybrid Dual-Mode Network for Closed- and Open-Set RFFI with Optimized VMD](https://mlsanity.com/paper/2507.12133/) (2025)
- [A Generic Machine Learning Framework for Radio Frequency Fingerprinting](https://mlsanity.com/paper/2510.09775/) (2025)
- [Generalizable and Interpretable RF Fingerprinting with Shapelet-Enhanced Large Language Models](https://mlsanity.com/paper/2602.03035/) (2026)
- [Model Forensics in AI-Native Wireless Networks: Taxonomy, Applications, and Case Study](https://mlsanity.com/paper/2605.14387/) (2026)
- [Hamiltonian-Inspired Attention Mechanism for Scalable RF Transmitter Fingerprinting](https://mlsanity.com/paper/2605.30364/) (2026)
- [Cross-Receiver Open-Set Radio Frequency Fingerprinting via Structure-First Adaptation](https://mlsanity.com/paper/2607.02567/) (2026)
- [Interpretable Feature Learning for RF Fingerprinting via Polar MKANs](https://mlsanity.com/paper/2608.19881/) (2026)
- [EMRB: A Multi-Level Benchmark for Evaluating LLM Reasoning over Raw Electromagnetic Signals](https://mlsanity.com/paper/2608.24086/) (2026)

## Abstract (arXiv)

RF fingerprinting leverages circuit-level variability of transmitters to identify them using signals they send. Signals used for identification are impacted by a wireless channel and receiver circuitry, creating additional impairments that can confuse transmitter identification. Eliminating these impairments or just evaluating them, requires data captured over a prolonged period of time, using many spatially separated transmitters and receivers. In this paper, we present WiSig; a large scale WiFi dataset containing 10 million packets captured from 174 off-the-shelf WiFi transmitters and 41 USRP receivers over 4 captures spanning a month. WiSig is publicly available, not just as raw captures, but as conveniently pre-processed subsets of limited size, along with the scripts and examples. A preliminary evaluation performed using WiSig shows that changing receivers, or using signals captured on a different day can significantly degrade a trained classifier's performance. While capturing data over more days or more receivers limits the degradation, it is not always feasible and novel data-driven approaches are needed. WiSig provides the data to develop and evaluate these approaches towards channel and receiver agnostic transmitter fingerprinting.