Neural Network-Driven Estimation of Hardware Impairments for Robust Wireless Device Identification

Haytham Albousayri, Bechir Hamdaoui, Weng-Keen Wong

Keywords: Wireless device identification, hardware impairment estimation, neural network-based optimization.

TL;DR: Estimating devices' impairments for RF fingerprinting to improve identification accuracy.

Abstract: We proposed a neural network (NN)-based framework for the joint estimation of hardware impairments in wireless devices. We validated the approach using real-world measurements from Bluetooth Low Energy (BLE) and WiFi devices. Experimental results show that the estimated impairments exhibit a stationary distribution after the warm-up phase, highlighting their stability and potential as device-specific fingerprints. Moreover, we demonstrated that these impairments provide a lightweight yet robust alternative to raw IQ samples for RF fingerprinting (RFFP), achieving an average improvement of 54% in classification accuracy across different domains.

Submission Number: 23