Low Complexity Deep Learning Models for LoRa Radio Frequency Fingerprinting

LoRaWAN devices are secured using traditional
cryptographic methods. However, the end devices are still
vulnerable to security attacks such as impersonation. To counter
these attacks, LoRa requires an additional layer of security at the
physical level. Deep Learning-based LoRa device identification
using Radio Frequency Fingerprinting is currently seen as a key
candidate for enhancing LoRa security at the physical layer. How-
ever, a more in-depth study of certain aspects of this approach is
required. Firstly, the appropriate LoRa signal representation has
to be chosen which provides high device identification accuracy.
Secondly, there is a need for deep learning models with less
computational complexity and high performance. This paper
contributes to the state-of-the-art in two ways: (1) we evaluate
various signal representations such as raw In-phase Quadrature
(IQ), Amplitude and Phase (A/φ), frequency domain (FFT), and
time-frequency domain (Spectrogram) (2) we show that an exist-
ing complex ResNet model can be optimized into a lightweight
model by tuning its parameters without compromising much on
the performance. We implement a different lightweight model for
IQ, FFT, and A/φ representations. We show that our optimized
ResNet model for spectrogram and proposed lightweight model
for sequential data can achieve an accuracy of over 97%.