Selçuk Taşcıoğlu

Radio frequency (RF) fingerprinting is regarded as a promising solution to improve wireless security, especially in applications where resource-limited devices are employed. Unlike steady-state signals, such as preambles or data, the use of short-duration transient signals for RF fingerprinting offers distinct advantages for systems with low latency and low complexity requirements. One of the challenges associated with transient-based methods in practice is achieving high performance while utilizing low-cost receivers. In this study, we demonstrate for the first time that the performance of transient-based RF fingerprinting can be enhanced by designing the filter chain in a software defined radio (SDR) receiver, taking into account the relevant signal characteristics. The performance analysis is conducted using transient-based identification signals captured by the SDR receiver, focusing on the sampling rate and duration of the identification signal. In the experiments, signals collected from twenty IEEE 802.11 transmitters are used. Experimental results indicate that so long as the receiver filter parameters and the duration of the identification signal are properly determined, a high classification performance exceeding 92% can be achieved for transient-based RF fingerprinting, even at sampling rates approaching the Nyquist limit.

In a real-world environment, multifunction radars (MFRs) pose major challenges to the electronic support system that is a part of cognitive electronic warfare. One of the main problems is deinterleaving of signals belonging to MFRs. The ability of MFRs to change their carrier frequency, pulse width, and pulse repetition interval (PRI) from pulse to pulse makes the deinterleaving task challenging. In this paper, an object detection based deinterleaving approach exploiting amplitude patterns caused by radar beam motions, which are determined based on radar antenna scan types, is proposed to deinterleave MFR signals. Amplitude patterns are created in two-dimensional images using amplitude (AMP) and time of arrival (TOA) parameters obtained from radar pulses. Deinterleaving is performed using a deep learning algorithm applied to these images. To the best of the authors’ knowledge, object detection based deinterleaving of radar signals using AMP-TOA images has not been considered so far. Contrary to the common approaches, in which searching for PRI patterns is required, amplitude patterns formed by the radar beam motions on the electronic support system are used in the proposed method. This enables robust identification of signals of MFRs having PRI, carrier frequency, and pulse width agility. With the proposed method, the intention of the radar, i.e., search or tracking, can be acquired in the deinterleaving stage, which ensures earlier situational awareness. The performance of the method is evaluated for varying number of radar signals from one to five through simulations, in which scenarios with missing pulses at different rates are considered. Simulation results demonstrate that, on the average, more than 0.98 mean average precision (mAP50) is achieved at 30% missing pulse rate. The performance of the method for search radar signals is slightly lower compared to that achieved for tracking radar signals due to the lower number of pulses received by the system. However, more than 0.93 AP50 is achieved for search radar signals in the presence of five radar signals with different missing pulse rates. Besides, real-time performance can be achieved using the proposed method on the GPU platform.

Quadrature mixing is widely used in wireless communication receivers since it provides a solution for image signal problem with low-cost implementations. Image signal is caused by phase and amplitude mismatches between in-phase (I) and quadrature (Q) paths of the receiver. This problem is known as I/Q imbalance and degrades the communication performance if not compensated. In this study, the impact of I/Q imbalance compensation on wireless communication performance is evaluated through experiments and simulations. Simulation results demonstrate that significant communication performance improvement can be achieved in terms of bit error rate (BER) and symbol error rate (SER) by compensating the I/Q imbalance properly. In the experiments, compensation is applied to the signals captured using a software defined radio with zero-IF architecture. Experimental results demonstrate that wireless transmission success rate for the zero-IF receiver is increased by compensating I/Q imbalance.

In this paper, a sequential transient detection method for radio frequency (RF) fingerprinting used in the identification of wireless devices is proposed. To the best knowledge of the authors, sequential detection of transient signals for RF fingerprinting has not been considered in the literature. The proposed method is based on an approximate implementation of the generalized likelihood ratio algorithm. The method can be implemented online in a recursive manner with low computational and memory requirements. The transients of wireless transmitters are detected by using the likelihood ratio of the observations without the requirement of any a priori knowledge about the transmitted signals. The performance of the method was evaluated using experimental data collected from 16 Wi-Fi transmitters and compared to those of two existing methods. The experimental test results showed that the proposed method can be used to detect the transient signals with a low detection delay. Our proposed method estimates transient starting points 20-times faster compared to an existing robust method, as well as providing a classification performance of a mean accuracy close to 95%.

In this paper, the effects DC offset in direct-conversion receivers for WLAN systems are analyzed using both experimental and simulation data. DC offset estimation is performed by using data-aided methods which are based on the short training sequence of WLAN preamble. In the simulations, DC offset and frequency offset estimations are carried out on the signals affected by frequency selective Rayleigh fading channel and additive white Gaussian noise. Estimation performance of the methods is compared for different SNR levels and frequency offset values in terms of mean square error. Experimental data which is in the form of WLAN packets and transmitted through a wireless channel is captured by using a software defined radio. The experimental performance of the DC offset compensation methods is evaluated in terms of transmission success ratio.

Radio frequency (RF) fingerprinting is considered as one of the promising techniques to enhance wireless security in the Internet of Things (IoT) applications. In this paper, a low-complexity RF fingerprinting method for classification of wireless IoT devices is proposed. The method is based on the energy spectrum of the transmitter turn-on transient signals from which unique characteristics of wireless devices are extracted. The number of spectral components to be used is determined through a proposed approach based on the estimated transient duration value. Transient duration estimation is achieved from the smoothed versions of the instantaneous amplitude characteristics of transmitter signals, which are obtained through a sliding windowaveraging method. Classification performance of the proposed spectral fingerprints is assessed using experimental data and described by a confusion matrix. The discrimination effectiveness of the spectral fingerprints is quantified by a class separability criterion and evaluated for different noise levels through Monte Carlo simulations. It is demonstrated that the proposed fingerprints outperform the classification performance of two existing fingerprints especially at low signal-to-noise ratio. Additionally, computational complexity analysis of the classifier using the proposed fingerprints is provided.

A novel approach for wideband spectrum segmentation is proposed. The proposed method, called segmented periodogram, is based on the posterior expectation of the piece-wise flat realizations of the underlying signal spectrum, which is obtained through the simulated samples drawn from the joint posterior using the reversible jump Markov chain Monte Carlo (RJMCMC) technique. The segmented periodogram is a coarse description of the spectrum, which can be further utilized to obtain desired spectral parameters. The proposed framework is suitable for cognitive radios (CRs) as it allows the use of knowledge built from past experiences in the form of marginal densities as prior initializations in future calculations.