Feature extraction approaches to RF fingerprinting for device identification in femtocells

Abstract

Radio frequency (RF) fingerprinting is a technique which attempts to extract a unique identifier from wireless signal transmissions in order to perform automated device identification by exploiting variations in the transmitted signal caused by hardware and manufacturing inconsistencies. The problem of signaling storms caused by increased core network signaling load due to idle mode cell camping on femtocells is addressed and a novel approach to RF fingerprinting is proposed as a solution to tackle this problem by identifying device model types. This study describes a large data set containing 54 Universal Mobile Telecommunications System (UMTS) user equipment (UE) devices (41 model types), the largest of its kind reported in the literature. This study also presents a novel feature extraction technique, which greatly improves identification accuracy over standard spectral approaches while limiting the number of random access channel (RACH) preambles required for identification. Accuracy of 99.8 percent was achieved. Importantly, the proposed technique can be implemented using today's low cost high-volume receivers and requires no manual performance tuning.