Passive Steady State RF Fingerprinting: A Cognitive Technique for Scalable Deployment of Co-channel Femto Cell Underlays

摘要

Recently, cellular operators have begun evaluating femto cells that aggressively reuse spectrum to cover a small spatial footprint (10m radius). A large-scale femto cell underlay network will increase an operator's total number of cells by two to three orders of magnitude and presents significant scaling problems in spectrum reuse. We argue that to achieve this scale and interoperability with the existing UMTS network and handsets, the femto cell must use novel yet simple cognitive techniques: sensing, smart handover and idle mode cell camping. We report in detail on the problem of signaling storms caused by increased core network-signaling load due to idle mode cell camping. We provide a novel passive RF fingerprinting technique as a solution to tackle the problem. The technique is based on frequency domain characteristics. Our technique detects the unique characteristics imbued in a signal as it passes through a transmit chain. We are the first to propose the use of discriminatory classifiers based on steady state spectral features. In laboratory experiments, we achieve 91% accuracy at 15dB SNR based on seven different models of UMTS user equipment. In the largest known laboratory experiment of its kind, we report an accuracy of 85% using our technique on twenty UMTS user equipment. This large test set includes 10 identical devices. Our technique can be implemented using today's low cost high-volume receivers and requires no manual performance tuning.