# A Generalizable Model-and-Data Driven Approach for Open-Set RFF Authentication

arXiv: https://arxiv.org/abs/2108.04436 · HTML: https://mlsanity.com/paper/2108.04436/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 50 references that are in the map.
- First posted on arXiv 10 Aug 2021.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Rethinking the Value of Network Pruning](https://mlsanity.com/paper/1810.05270/) (2018)
- [Recent Advances in Open Set Recognition: A Survey](https://mlsanity.com/paper/1811.08581/) (2018)
- [L2-constrained Softmax Loss for Discriminative Face Verification](https://mlsanity.com/paper/1703.09507/) (2017)
- [Neural Approximate Sufficient Statistics for Implicit Models](https://mlsanity.com/paper/2010.10079/) (2020)
- [Neural Adaptive Sequential Monte Carlo](https://mlsanity.com/paper/1506.03338/) (2015)
- [Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations](https://mlsanity.com/paper/2005.00954/) (2020)
- [Machine Learning Approach to RF Transmitter Identification](https://mlsanity.com/paper/1711.01559/) (2017)
- [Wireless Physical-Layer Identification: Modeling and Validation](https://mlsanity.com/paper/1510.08485/) (2015)
- [Learning-Aided Physical Layer Authentication as an Intelligent Process](https://mlsanity.com/paper/1808.02456/) (2018)
- [Deep Learning Approaches for Open Set Wireless Transmitter Authorization](https://mlsanity.com/paper/2002.07777/) (2020)
- [Security Formalizations and Their Relationships for Encryption and Key Agreement in Information-Theoretic Cryptography](https://mlsanity.com/paper/1410.1120/) (2014)

## Built on by (most cited first, 7 of 7)

- [MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data](https://mlsanity.com/paper/2310.02275/) (2023)
- [Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics](https://mlsanity.com/paper/2208.02724/) (2022)
- [ISAC-NET: Model-driven Deep Learning for Integrated Passive Sensing and Communication](https://mlsanity.com/paper/2307.15074/) (2023)
- [Adversarial Attacks Against Deep Learning-Based Radio Frequency Fingerprint Identification](https://mlsanity.com/paper/2512.12002/) (2025)
- [HyDRA: A Hybrid Dual-Mode Network for Closed- and Open-Set RFFI with Optimized VMD](https://mlsanity.com/paper/2507.12133/) (2025)
- [Few-Shot Specific Emitter Identification via Integrated Complex Variational Mode Decomposition and Spatial Attention Transfer](https://mlsanity.com/paper/2512.16786/) (2025)
- [Rapid LoRA Aggregation for Wireless Channel Adaptation in Open-Set Radio Frequency Fingerprinting](https://mlsanity.com/paper/2604.12834/) (2026)

## Abstract (arXiv)

Radio-frequency fingerprints~(RFFs) are promising solutions for realizing low-cost physical layer authentication. Machine learning-based methods have been proposed for RFF extraction and discrimination. However, most existing methods are designed for the closed-set scenario where the set of devices is remains unchanged. These methods can not be generalized to the RFF discrimination of unknown devices. To enable the discrimination of RFF from both known and unknown devices, we propose a new end-to-end deep learning framework for extracting RFFs from raw received signals. The proposed framework comprises a novel preprocessing module, called neural synchronization~(NS), which incorporates the data-driven learning with signal processing priors as an inductive bias from communication-model based processing. Compared to traditional carrier synchronization techniques, which are static, this module estimates offsets by two learnable deep neural networks jointly trained by the RFF extractor. Additionally, a hypersphere representation is proposed to further improve the discrimination of RFF. Theoretical analysis shows that such a data-and-model framework can better optimize the mutual information between device identity and the RFF, which naturally leads to better performance. Experimental results verify that the proposed RFF significantly outperforms purely data-driven DNN-design and existing handcrafted RFF methods in terms of both discrimination and network generalizability.