Enabling Fake Base Station Detection through Sample-based Higher Order Noise Statistics

Unpublished / Preprint

Publication Details

Author(s): Ali A, Fischer G
Publication year: 2019
Language: English


This paper presents computationally efficient fake
base station (FBS) detection scheme through higher order
statistical analysis at the user equipment (UE) side. In the
proposed RF fingerprinting detection scheme, the UE inspects
surrounding base stations (BS) by first extracting noise from the
received signal through novel sample-based parametric estimation
technique and then measuring the noise structuredness with the
aid of fourth order moment i.e. kurtosis over the estimated noise
samples. This reveals unique RF fingerprints of the legitimized
regular base station (RBS) in terms of hardware impairments and
resultantly indicates minimal impact from the non-linearities due
to employment of costly and high precision analog and mixedsignal components, strong network-synchronous clock and a
sophisticated linearization effort around the power amplifier (PA).
In contrary, FBS exhibits different RF fingerprints containing
larger amount of non-linearities in the received signal due to
presence of large impairments. With the help of actual
measurement results from RBS and various software defined
radio (SDR) based FBS at different cellular standards and by
defining a critical threshold of detection, we show that an FBS
deviates a lot from the ideal Gaussian noise distribution and
constitutes of multivariate distributions, whereas an RBS show
minimal deviation from the reference and contains univariate
noise distribution. We further calculate the observation sample
time required to detect an FBS in an optimal MMSE sense and
indicate with the help of results that the minimum time to identify
a fake cell is 10ms.

FAU Authors / FAU Editors

Ali, Arslan
Lehrstuhl für Technische Elektronik
Fischer, Georg Prof. Dr.-Ing.
Professur für Technische Elektronik

Last updated on 2019-01-05 at 21:44