829 research outputs found
Spatial & Temporal Agnostic Deep-Learning Based Radio Fingerprinting
Radio fingerprinting is a technique that validates wireless devices based on their unique radio frequency (RF) signals. This method is highly feasible because RF signals carry distinct hardware variations introduced during manufacturing. The security and trustworthiness of current and future wireless networks heavily rely on radio fingerprinting. In addition to identifying individual devices, it can also differentiate mission-critical targets. Despite significant efforts in the literature, existing radio fingerprinting methods require improved robustness, scalability, and resilience. This study focuses on the challenges of spatial-temporal variations in the wireless environment. Many prior approaches overlook the complex numerical structure of the in-phase and quadrature (I/Q) data by treating real and imaginary components separately. This approach results in the loss of essential information encoded in the signal\u27s phase and amplitude, leading to lower accuracy. This thesis proposes several enhancements. First, we treat the entire complex structure of the I/Q data as a single input to a complex-valued convolutional neural network (CVNN), thereby improving the model\u27s accuracy. Second, conduct extensive experiments to determine optimal pre-processing parameters, ensuring that over-optimistic conclusions about RF fingerprinting performance are avoided. Third, we compare various activation functions and transfer learning-based fine-tuning and a triplet network to address the variations the wireless environment introduces in scenarios involving different locations and times. We use the concept of a ``device rank\u27\u27 metric to perform device identification with certainty based on RF fingerprinting. Our work concretely proves that CVNN outperforms CNN for radio fingerprinting. Concatenated Rectified Linear Units (CReLU) activation function and fine-tuning-based transfer learning perform the best for cross-location and time device fingerprinting.
Adviser: Nirnimesh Ghos
Preprint: Using RF-DNA Fingerprints To Classify OFDM Transmitters Under Rayleigh Fading Conditions
The Internet of Things (IoT) is a collection of Internet connected devices
capable of interacting with the physical world and computer systems. It is
estimated that the IoT will consist of approximately fifty billion devices by
the year 2020. In addition to the sheer numbers, the need for IoT security is
exacerbated by the fact that many of the edge devices employ weak to no
encryption of the communication link. It has been estimated that almost 70% of
IoT devices use no form of encryption. Previous research has suggested the use
of Specific Emitter Identification (SEI), a physical layer technique, as a
means of augmenting bit-level security mechanism such as encryption. The work
presented here integrates a Nelder-Mead based approach for estimating the
Rayleigh fading channel coefficients prior to the SEI approach known as RF-DNA
fingerprinting. The performance of this estimator is assessed for degrading
signal-to-noise ratio and compared with least square and minimum mean squared
error channel estimators. Additionally, this work presents classification
results using RF-DNA fingerprints that were extracted from received signals
that have undergone Rayleigh fading channel correction using Minimum Mean
Squared Error (MMSE) equalization. This work also performs radio discrimination
using RF-DNA fingerprints generated from the normalized magnitude-squared and
phase response of Gabor coefficients as well as two classifiers. Discrimination
of four 802.11a Wi-Fi radios achieves an average percent correct classification
of 90% or better for signal-to-noise ratios of 18 and 21 dB or greater using a
Rayleigh fading channel comprised of two and five paths, respectively.Comment: 13 pages, 14 total figures/images, Currently under review by the IEEE
Transactions on Information Forensics and Securit
Data-driven design of intelligent wireless networks: an overview and tutorial
Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves
Wiometrics: Comparative Performance of Artificial Neural Networks for Wireless Navigation
Radio signals are used broadly as navigation aids, and current and future
terrestrial wireless communication systems have properties that make their
dual-use for this purpose attractive. Sub-6 GHz carrier frequencies enable
widespread coverage for data communication and navigation, but typically offer
smaller bandwidths and limited resolution for precise estimation of geometries,
particularly in environments where propagation channels are diffuse in time
and/or space. Non-parametric methods have been employed with some success for
such scenarios both commercially and in literature, but often with an emphasis
on low-cost hardware and simple models of propagation, or with simulations that
do not fully capture hardware impairments and complex propagation mechanisms.
In this article, we make opportunistic observations of downlink signals
transmitted by commercial cellular networks by using a software-defined radio
and massive antenna array mounted on a passenger vehicle in an urban non
line-of-sight scenario, together with a ground truth reference for vehicle
pose. With these observations as inputs, we employ artificial neural networks
to generate estimates of vehicle location and heading for various artificial
neural network architectures and different representations of the input
observation data, which we call wiometrics, and compare the performance for
navigation. Position accuracy on the order of a few meters, and heading
accuracy of a few degrees, are achieved for the best-performing combinations of
networks and wiometrics. Based on the results of the experiments we draw
conclusions regarding possible future directions for wireless navigation using
statistical methods
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