# Wireless Physical-Layer Identification: Modeling and Validation

arXiv: https://arxiv.org/abs/1510.08485 · HTML: https://mlsanity.com/paper/1510.08485/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2015.

## Built on by (most cited first, 10 of 10)

- [A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges](https://mlsanity.com/paper/2201.00680/) (2022)
- [Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey](https://mlsanity.com/paper/2101.10181/) (2021)
- [A Generalizable Model-and-Data Driven Approach for Open-Set RFF Authentication](https://mlsanity.com/paper/2108.04436/) (2021)
- [Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics](https://mlsanity.com/paper/2208.02724/) (2022)
- [Open Set RF Fingerprinting using Generative Outlier Augmentation](https://mlsanity.com/paper/2108.13099/) (2021)
- [Noise-Robust Radio Frequency Fingerprint Identification Using Denoise Diffusion Model](https://mlsanity.com/paper/2503.05514/) (2025)
- [AoA-Based Physical Layer Authentication in Analog Arrays under Impersonation Attacks](https://mlsanity.com/paper/2407.08282/) (2024)
- [Exploiting Radio Frequency Fingerprints for Device Identification: Tackling Cross-receiver Challenges in the Source-data-free Scenario](https://mlsanity.com/paper/2512.16648/) (2025)
- [Real-time Wireless Transmitter Authorization: Adapting to Dynamic Authorized Sets with Information Retrieval](https://mlsanity.com/paper/2111.02584/) (2021)
- [Rapid LoRA Aggregation for Wireless Channel Adaptation in Open-Set Radio Frequency Fingerprinting](https://mlsanity.com/paper/2604.12834/) (2026)

## Abstract (arXiv)

The wireless physical-layer identification (WPLI) techniques utilize the unique features of the physical waveforms of wireless signals to identify and classify authorized devices. As the inherent physical layer features are difficult to forge, WPLI is deemed as a promising technique for wireless security solutions. However, as of today it still remains unclear whether existing WPLI techniques can be applied under real-world requirements and constraints. In this paper, through both theoretical modeling and experiment validation, the reliability and differentiability of WPLI techniques are rigorously evaluated, especially under the constraints of state-of-art wireless devices, real operation environments, as well as wireless protocols and regulations. Specifically, a theoretical model is first established to systematically describe the complete procedure of WPLI. More importantly, the proposed model is then implemented to thoroughly characterize various WPLI techniques that utilize the spectrum features coming from the non-linear RF-front-end, under the influences from different transmitters, receivers, and wireless channels. Subsequently, the limitations of existing WPLI techniques are revealed and evaluated in details using both the developed theoretical model and in-lab experiments. The real-world requirements and constraints are characterized along each step in WPLI, including i) the signal processing at the transmitter (device to be identified), ii) the various physical layer features that originate from circuits, antenna, and environments, iii) the signal propagation in various wireless channels, iv) the signal reception and processing at the receiver (the identifier), and v) the fingerprint extraction and classification at the receiver.