# Cross-Receiver Generalization for RF Fingerprint Identification via Feature Disentanglement and Adversarial Training

arXiv: https://arxiv.org/abs/2510.09405 · HTML: https://mlsanity.com/paper/2510.09405/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 40 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Generalizing to unseen domains via distribution matching](https://mlsanity.com/paper/1911.00804/) (2019)
- [RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction](https://mlsanity.com/paper/2408.08593/) (2024)
- [ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks](https://mlsanity.com/paper/1812.01124/) (2018)
- [WiSig: A Large-Scale WiFi Signal Dataset for Receiver and Channel Agnostic RF Fingerprinting](https://mlsanity.com/paper/2112.15363/) (2021)
- [RadioDiff-k2: Helmholtz Equation Informed Generative Diffusion Model for Multi-Path Aware Radio Map Construction](https://mlsanity.com/paper/2504.15623/) (2025)
- [Towards Scalable and Channel-Robust Radio Frequency Fingerprint Identification for LoRa](https://mlsanity.com/paper/2107.02867/) (2021)
- [Radio Frequency Fingerprint Identification for LoRa Using Spectrogram and CNN](https://mlsanity.com/paper/2101.01668/) (2020)
- [Towards Receiver-Agnostic and Collaborative Radio Frequency Fingerprint Identification](https://mlsanity.com/paper/2207.02999/) (2022)
- [Towards Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification](https://mlsanity.com/paper/2207.03001/) (2022)
- [Robust Wireless Fingerprinting via Complex-Valued Neural Networks](https://mlsanity.com/paper/1905.09388/) (2019)
- [GAN-RXA: A Practical Scalable Solution to Receiver-Agnostic Transmitter Fingerprinting](https://mlsanity.com/paper/2303.14312/) (2023)
- [Multi-Channel Attentive Feature Fusion for Radio Frequency Fingerprinting](https://mlsanity.com/paper/2303.10691/) (2023)
- [Domain Generalization for Cross-Receiver Radio Frequency Fingerprint Identification](https://mlsanity.com/paper/2411.03636/) (2024)

## Built on by (most cited first, 1 of 1)

- [Interpretable Feature Learning for RF Fingerprinting via Polar MKANs](https://mlsanity.com/paper/2608.19881/) (2026)

## Abstract (arXiv)

Radio frequency fingerprint identification (RFFI) is a key technique for wireless network security, leveraging intrinsic hardware imperfections to enable transmitter identification. Although deep neural networks are effective at extracting discriminative RF features, their performance is significantly affected by receiver-induced variability in practical deployments. In real-world scenarios, RF signals inherently entangle transmitter-specific characteristics with receiver-dependent distortions, leading models to capture receiver-related patterns when training and evaluation are conducted on the same device. Consequently, replacing the receiver during deployment often results in notable performance degradation. To address this issue, we propose a cross-receiver robust RFFI framework that explicitly disentangles transmitter-specific and receiver-specific representations. The proposed method integrates adversarial domain alignment with receiver-aware regularization to suppress residual receiver information in transmitter features while enforcing intra-receiver consistency in receiver-specific representations. A feature separation constraint is further introduced to decouple the two components in the latent space. Extensive experiments on multi-receiver WiFi datasets demonstrate that the proposed method consistently outperforms state-of-the-art baselines under cross-receiver evaluation and significantly improves robustness to receiver replacement.