EPS: Distinguishable IQ Data Representation for Domain-Adaptation Learning of Device Fingerprints. (arXiv:2308.04467v1 [cs.CR])

Deep learning (DL)-based RF fingerprinting (RFFP) technology has emerged as a
powerful physical-layer security mechanism, enabling device identification and
authentication based on unique device-specific signatures that can be extracted
from the received RF signals. However, DL-based RFFP methods face major
challenges concerning their ability to adapt to domain (e.g., day/time,
location, channel, etc.) changes and variability. This work proposes a novel IQ
data representation and feature design, termed Double-Sided Envelope Power
Spectrum or EPS, that is proven to overcome the …

authentication challenges data deep learning device domain eps fingerprinting fingerprints identification major physical representation security signals signatures technology