Title: PRLS-RFF: Physically Consistent Representation Learning with Self-Supervised Pretraining for RF Fingerprinting
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Selective State-Space Models (Mamba);Radio-Frequency Fingerprinting (RFF);Cross-Domain Generalization; Open-World Device Recognition; Wireless Security

Abstract: Under domain shifts and open-world conditions, reliably re-identifying radio frequency (RF) devices is essential for wireless security. As a hardware-rooted physical-layer signature, the RF fingerprint has been widely used for device re-identification. However, supervised RF fingerprint identification (RFFI) models often overfit acquisition artifacts and rely on extensive supervision, leading to sharp cross-domain performance drops and weak open-world behavior. To address these limitations, we introduce PRLS-RFF, which targets physically consistent RF fingerprint representations. We design a dual-stream Mamba-based backbone with physics-consistent, multi-view perturbations to encode representation-level invariances. Additionally, to better capture the structure of RF fingerprints (RFFs) across transient and steady-state regimes, the backbone fuses time-domain and time--frequency features using efficient long-context modeling. As a result, the learned representations are domain-robust, which can support reliable open-set recognition. To validate its robustness, we conduct extensive experiments on several public datasets and observe the performance surpassing state-of-the-art models in both cross-domain identification and open-set recognition tasks.

TL;DR: Physics-consistent multi-view self-supervised contrastive pretraining for a Mamba dual-stream RF model, learning hardware-invariant fingerprints and improving cross-domain classification & open-world detection with fewer labels.