# Domain Generalization for Cross-Receiver Radio Frequency Fingerprint Identification

arXiv: https://arxiv.org/abs/2411.03636 · HTML: https://mlsanity.com/paper/2411.03636/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2024.

## Built on by (most cited first, 4 of 4)

- [Cross-Receiver Generalization for RF Fingerprint Identification via Feature Disentanglement and Adversarial Training](https://mlsanity.com/paper/2510.09405/) (2025)
- [A Generic Machine Learning Framework for Radio Frequency Fingerprinting](https://mlsanity.com/paper/2510.09775/) (2025)
- [Cross-Receiver Open-Set Radio Frequency Fingerprinting via Structure-First Adaptation](https://mlsanity.com/paper/2607.02567/) (2026)
- [Physics-Informed Structure Anchoring With Capture-Aware Prototype Calibration for Cross-Environment RF Fingerprinting](https://mlsanity.com/paper/2607.09760/) (2026)

## Abstract (arXiv)

Radio Frequency Fingerprint Identification (RFFI) technology uniquely identifies emitters by analyzing unique distortions in the transmitted signal caused by non-ideal hardware. Recently, RFFI based on deep learning methods has gained popularity and is seen as a promising way to address the device authentication problem for Internet of Things (IoT) systems. However, in cross-receiver scenarios, where the RFFI model is trained over RF signals from some receivers but deployed at a new receiver, the alteration of receivers' characteristics would lead to data distribution shift and cause significant performance degradation at the new receiver. To address this problem, we first perform a theoretical analysis of the cross-receiver generalization error bound and propose a sufficient condition, named Separable Condition (SC), to minimize the classification error probability on the new receiver. Guided by the SC, a Receiver-Independent Emitter Identification (RIEI)model is devised to decouple the received signals into emitter-related features and receiver-related features and only the emitter-related features are used for identification. Furthermore, by leveraging federated learning, we also develop a FedRIEI model to eliminate the need for centralized collection of raw data from multiple receivers. Experiments on two real-world datasets demonstrate the superiority of our proposed methods over some baseline methods.