Transmitter Identification Research Articles

Probabilistic Analysis on Successive Cancellation-Assisted Transmitter Identification in SFN With Randomly Distributed Co-Channel Interferers

This paper investigates the detection error of radio frequency (RF)-watermark type transmitter identification (TxID) signal in a single frequency network (SFN). Based on the Cramer-Rao bound (CRB) of TxID detection, closed-form failure probabilities for TxID detection are derived. In order to reflect the practical network condition, the interference from Poisson-distributed out-of-guard interval transmitters is accounted for TxID detection failure probability (TDFP). The proposed approach consequently examines the possible TDFP gain that can be obtained by applying successive preamble cancellation to the TxID detection. Numerical results reveal that the performance of the preamble cancellation-assisted technique is dependent on the threshold signal-to-interference noise ratio (SINR) for preamble signal detection. Nevertheless, the assistance of preamble cancellation is verified to guarantee more than 4 dB improvement of TxID detection capability under practical preamble signal configurations.

Wireless Transmitter Identification Based on Device Imperfections

Mining useful patterns from databases is an important research topic. The research in utility mining mostly focuses on discovering patterns of high value in large databases, and analyzing the important factors in a data mining process. This idea is applied to the wireless device identification in this paper. Radio Frequency Fingerprint (RFF) reflects differences between transmitter hardware components. It contains rich non-linear characteristics of the internal components of the transmitter. Small differences and inaccuracies in the manufacturing process determine the unique characteristic contained in the transmitted signal. The device can be identified by the signal transmitted by the wireless device. In this paper, the generation mechanism of RFF is analyzed and two pattern mining algorithms are used to extract useful information from wireless signals for device identification. Then, a real communication transmitter link is established to study the effect of different components of a transmitter. The signals are acquired from the transmitters with different components replaced, including the amplifier, the bandpass filter, and the local oscillator. Finally, the influence of different components and pattern mining methods are evaluated.

RFAL: Adversarial Learning for RF Transmitter Identification and Classification

Recent advances in wireless technologies have led to several autonomous deployments of such networks. As nodes across distributed networks must co-exist, it is important that all transmitters and receivers are aware of their radio frequency (RF) surroundings so that they can adapt their transmission and reception parameters to best suit their needs. To this end, machine learning techniques have become popular as they can learn, analyze and predict the RF signals and associated parameters that characterize the RF environment. However, in the presence of adversaries, malicious activities such as jamming and spoofing are inevitable, making most machine learning techniques ineffective in such environments. In this paper we propose the Radio Frequency Adversarial Learning (RFAL) framework for building a robust system to identify rogue RF transmitters by designing and implementing a generative adversarial net (GAN). We hope to exploit transmitter specific “signatures” like the in-phase (I) and quadrature (Q) imbalance (i.e., the I/Q imbalance ) present in all transmitters for this task, by learning feature representations using a deep neural network that uses the I/Q data from received signals as input. After detection and elimination of the adversarial transmitters RFAL further uses this learned feature embedding as “fingerprints” for categorizing the trusted transmitters. More specifically, we implement a generative model that learns the sample space of the I/Q values of known transmitters and uses the learned representation to generate signals that imitate the transmissions of these transmitters. We program 8 universal software radio peripheral (USRP) software defined radios (SDRs) as trusted transmitters and collect “over-the-air” raw I/Q data from them using a Realtek Software Defined Radio (RTL-SDR), in a laboratory setting. We also implement a discriminator model that discriminates between the trusted transmitters and the counterfeit ones with 99.9% accuracy and is trained in the GAN framework using data from the generator. Finally, after elimination of the adversarial transmitters, the trusted transmitters are classified using a convolutional neural network (CNN), a fully connected deep neural network (DNN) and a recurrent neural network (RNN) to demonstrate building of an end-to-end robust transmitter identification system with RFAL. Experimental results reveal that the CNN, DNN, and RNN are able to correctly distinguish between the 8 trusted transmitters with 81.6%, 94.6% and 97% accuracy respectively. We also show that better “trusted transmission” classification accuracy is achieved for all three types of neural networks when data from two different types of transmitters (different manufacturers) are used rather than when using the same type of transmitter (same manufacturer).

Detection Schemes for ATSC 3.0 Transmitter Identification in Single Frequency Network

Single frequency network (SFN) enables efficient use of spectrum by allocating the same frequency among multiple transmitters. In order to efficiently design and manage the SFN, the Advanced Television Systems Committee (ATSC) 3.0 physical layer standard has adopted transmitter identification (TxID) technology which allows to measure the individual and/or whole network channel response, such as power and time delay of the broadcast signal from transmitters. Since TxID is embedded into the host ATSC 3.0 frame as radio frequency (RF) watermark, it is transparent to legacy ATSC 3.0 receivers and only a specially designed TxID analyzer is able to detect it. This paper presents new TxID detection methods that improve the detection performance of ATSC 3.0 TxID signal by resolving the problems and limitation of the conventional TxID detection method. The superiorities of the proposed detection methods are verified through computer simulation, laboratory test, and field trial.

Efficient ATSC 3.0 TxID Signal Detection for Single Frequency Networks

Single-frequency network (SFN) can be applied to advanced television systems committee (ATSC) 3.0-based terrestrial broadcasting systems to improve the service availability and quality of service without additional spectral resources. The main problem of SFN planning is the co-channel interference caused by multiple transmitters that use the same radio frequency for signal transmission and reception. The effect of the co-channel interference can be minimized by adjusting the network delay and transmit power of multiple transmitters within the SFN coverage area. To adjust the delay and power, it is necessary to estimate the channel impulse response (CIR) from each transmitter to a receiver. To support this process, a transmitter identification (TxID) signal is embedded in an original ATSC 3.0 signal. At the receiver, the individual CIR may be measured by a correlation-process-based TxID detection. However, since the correlation process may require large computational efforts, it is a quite challenging work to design a TxID detection algorithm with reduced complexity. This paper proposes a low-complexity TxID detection algorithm. The design and performance of the proposed algorithm is investigated and analyzed through numerical simulations. The simulation results indicate that the proposed algorithm can offer almost the same performance as the conventional TxID detection algorithm with significantly reduced complexity.

Selective Mapping for Robust Data Transmission Using TxID Signal

For Advanced Television Systems Committee (ATSC) digital terrestrial broadcasting systems, a transmitter identification (TxID) technique is introduced to analyze interference among broadcasting transmitters in single frequency networks (SFN). Based on the TxID analysis results, the transmit power and emission time delay of each broadcasting transmitter can be adjusted to minimize the effects of co- and adjacent- channel interference. For this reason, TxID has been recognized as an important feature in SFN configurations for ATSC 1.0 and 3.0 standards. Recently, there has been increasing interest in employing TxID for data transmission. This paper proposes a TxID based data transmission method for use with ATSC 1.0 and 3.0 standards. The major advantage of this data transmission method is its robustness while maintaining TxID properties. Simulations and laboratory tests demonstrate that the proposed technique can achieve a robust data transmission with backward compatibility to legacy ATSC 1.0 and 3.0 receivers.

Machine Learning Approach to RF Transmitter Identification

With the development and widespread use of wireless devices in recent years (mobile phones, Internet of Things, Wi-Fi), the electromagnetic spectrum has become extremely crowded. In order to counter security threats posed by rogue or unknown transmitters, it is important to identify RF transmitters not by the data content of the transmissions but based on the intrinsic physical characteristics of the transmitters. RF waveforms represent a particular challenge because of the extremely high data rates involved and the potentially large number of transmitters present in a given location. These factors outline the need for rapid fingerprinting and identification methods that go beyond the traditional hand-engineered approaches. In this study, we investigate the use of machine learning (ML) strategies to the classification and identification problems, and the use of wavelets to reduce the amount of data required. Four different ML strategies are evaluated: deep neural nets (DNN), convolutional neural nets (CNN), support vector machines (SVM), and multi-stage training (MST) using accelerated Levenberg-Marquardt (A-LM) updates. The A-LM MST method preconditioned by wavelets was by far the most accurate, achieving 100% classification accuracy of transmitters, as tested using data originating from 12 different transmitters. We discuss strategies for extension of MST to a much larger number of transmitters.

Impersonation Detection in Line-of-Sight Underwater Acoustic Sensor Networks

This work considers a line-of-sight underwater acoustic sensor network (UWASN) consisting of $M$ underwater sensor nodes randomly deployed according to uniform distribution within a vertical half-disc (the so-called trusted zone). The sensor nodes report their sensed data to a sink node on water surface on a shared underwater acoustic (UWA) reporting channel in a time-division multiple-access (TDMA) fashion, while an active-yet-invisible adversary (so-called Eve) is present in the close vicinity who aims to inject malicious data into the system by impersonating some Alice node. To this end, this work first considers an additive white Gaussian noise (AWGN) UWA channel, and proposes a novel, multiple-features based, two-step method at the sink node to thwart the potential impersonation attack by Eve. Specifically, the sink node exploits the noisy estimates of the distance, the angle of arrival, and the location of the transmit node as device fingerprints to carry out a number of binary hypothesis tests (for impersonation detection) as well as a number of maximum likelihood hypothesis tests (for transmitter identification when no impersonation is detected). We provide closed-form expressions for the error probabilities (i.e., the performance) of most of the hypothesis tests. We then consider the case of a UWA with colored noise and frequency-dependent pathloss, and derive a maximum-likelihood (ML) distance estimator as well as the corresponding Cramer-Rao bound (CRB). We then invoke the proposed two-step, impersonation detection framework by utilizing distance as the sole feature. Finally, we provide detailed simulation results for both AWGN UWA channel and the UWA channel with colored noise. Simulation results verify that the proposed scheme is indeed effective for a UWA channel with colored noise and frequency-dependent pathloss.

Passive Tracking Algorithm with Transmitter Attribute Information

Traditional passive tracking algorithms are usually subjected to problems such as the poor measurement accuracy of parameters of target motion, complex algorithm, instable tracking precision under complex circumstances. Aiming at this issue, in this paper we combined the dual function of transmitter identification and target tracking in passive reconnaissance, discussed the selection of transmitter eigenvector in passive tracking based on transmitter attribute information, established the passive tracking algorithm framework combined with transmitter attribute information, and then investigated passive tracking arithmetic model based on statistical regulation and ratiocinative regulation respectively, in the hope of providing new idea for passive target tracking.

Radio Transmitter Identification Based on Collaborative Representation

Benefiting from the correlation among the samples, a method of radio transmitter identification based on collaborative representation is put forward in this paper. Firstly, we extract the square integral bispectra features to characterise the nuances of radio transmitters in the feature space. Secondly, based on collaborative representation, the sparse coefficient is obtained easily. At last, benefiting from the discrimination information of coefficients, a classifier is constructed for the final radio transmitter identification. On the actual collected dataset from ten FM radios which belong to the same model and manufacturer, the robust identification performances verify the effectiveness of our method.

ATSC 3.0 Transmitter Identification Signals and Applications

In the ATSC 3.0 PHY layer standard, a transmitter identification (TxID) signal is defined in order to provide the identification of an ATSC 3.0 transmitter. TxID signal can also be used to find the co-channel and adjacent interference signals, to assist accurate location finding calculation, and to obtain the channel estimation for each transmitter, which can help local service (e.g., targeted advertisement) reception, as well as for emergency alert. For efficient use of spectrum and quality of service improvement, single frequency network (SFN) where all transmitters share a single RF channel is often implemented. The recently developed ATSC 3.0 physical layer standard has been designed to support SFN. For efficient designing, fine-tuning and operating an SFN, it is crucial to identify each transmitter, as well as to adjust transmitting power and emission time of each transmitter. This paper presents TxID for ATSC 3.0, and analyzes its detection performance under very low signal to noise ratio environments and other applications, such as location finding, and channel estimation etc. for each SFN transmitter.

Novel Fast Blind Channel Estimation and Hessian Analysis for Transmitter Identification of Digital Television Signals

The transmitter identification (Tx-ID) technique has been widely addressed in the modern advanced television systems committee digital television (DTV) standards. Kasami sequences are adopted as the Tx-ID sequences due to the favorable code capacity. However, the conventional cross-correlation based Tx-ID technique cannot combat the realistic multipath channel impairment. Therefore, in this paper, we propose a new least-square Tx-ID approach, which is demonstrated to be very robust when the multipath channels are encountered. We also establish the new Hessian analysis to provide the range for appropriate injection levels accordingly. Based on this new theoretical study, we design new efficient (fast) algorithms to determine the maximum allowable channel length given a particular Kasami Tx-ID sequence. These algorithms and theoretical results can be very useful for the future DTV technologies.

Field Test Results of Additional Data Transmission for the ATSC Terrestrial DTV System

In this paper, we explain an additional data transmission (ADT) increasing the data capacity of the advanced television systems committee terrestrial digital television (DTV) system, present its field test results conducted in Jeju Island, Korea, and analyze the results in different scenarios. The ADT system can offer an additional data of rate from a few hundred bits per second (bps) to a few megabits per second based on a transmitter identification (TxID) or successive interference cancellation (SIC) techniques. For the field test, the field strength and the required carrier to noise ratio for a stable reception were measured at each test point. Based on the field test, we verify that the TxID- and SIC-based ADT systems can provide an additional data link supporting 160 bps data rate, which is at least 13 dB robust compared to the conventional DTV system, and 2 Mbps data rate which is about 3 dB worse than the conventional DTV system, respectively.

Poverty and the Formation of Roma Identity in Hungary: Evidence from a Representative Panel Survey of Adolescents

This paper reports the findings from an analysis of the ethnic identification choices of adolescents in Hungary. Using a representative panel survey of adolescents in Hungary we test the hypothesis that poverty shapes how adolescents identify and conditions the transmission of identification in families. Our results indicate that Roma identification is part of dual Roma and Hungarian identification and there is substantial variation in identification both across generations and for respondents across survey waves. We find that children of mixed-ethnic families are more likely identify themselves as Roma in poor families than in more affluent ones. Our longitudinal analyses indicate that changes in economic hardship are statistically significantly associated with changes in ethnic identification, but the estimated magnitudes are modest.

Additional Data Transmission Based on Sequence Phase Modulation for the ATSC Terrestrial DTV System

Several previous studies have demonstrated that additional low-rate data can be transmitted in Advanced Television Systems Committee (ATSC) terrestrial digital television (DTV) systems by modulating the polarity of the transmitter identification (TxID) signal. Previously proposed additional-data transmission schemes can offer an additional reliable transmission channel, and thus they have a very large coverage area compared to that of a DTV signal. Unfortunately, as the cost of a reliable transmission, they have a limitation in their maximum data rate. In this paper, we propose a new additional data transmission scheme based on sequence phase modulation (SPM) to increase the data rate. The proposed scheme exploits not only the polarity but also the phase of a TxID signal, and thus it significantly enhances the data rate compared to conventional schemes.

New method for individual communication transmitter identification

Under the condition of low signal-noise-ratio,the individual features of communication transmitter in steady-state signal are covered very easily,and hard to be extracted and identified.In regard to this problem,considering the fact that oscillators used in different communication transmitters have unequal frequency stability,a new method based on fractal dimension and Support Vector Machine(SVM) was proposed.After oversampling IF(Intermediate Frequency) signal,information dimension was extracted as features,and then SVM classifier was designed to realize the automatic identification of unknown samples.To some extent,the feature was robust under AWGN(Additive White Gaussian Noise).The computer simulation shows that the method has good performance on classifying five PSK signals with the same order and the frequency stability difference of 0.01 ppm under 3 dB,its accuracy being 95%.

Augmented Data Transmission Based on Low Density Parity Check Code for the ATSC Terrestrial DTV System

In this paper, we propose a novel augmented data transmission (ADT) system based on low density parity check (LDPC) code for an additional data stream transmission in the Advanced Television Systems Committee (ATSC) terrestrial digital television system. The LDPC code-based ADT system can provide additional data of rate up to 3 megabits per second (Mbps), which is significantly improved over the conventional data transmission schemes used in the transmitter identification system. Based on the laboratory test, we verify that the LDPC code-based ADT system is only about 1.1 dB away from Shannon limit.

Kasami sequence studies for DTV transmitter identification

The transmitter identification of the DTV systems becomes crucial nowadays. Transmitter identification (TxID, or transmitter fingerprinting) technique is used to detect, diagnose and classify the operating status of any radio transmitter of interest. A pseudo random sequence was proposed to be embedded into the DTV signal before transmission. Thus, the transmitter identification can be realized by invoking the cross-correlation functions between the received signal and the possible candidates of the pseudo random sequences. Gold sequences and Kasami sequences are two excellent candidates for the transmitter ID sequences as they provide a large family of nearly-orthogonal codes. In order to investigate the sensitivity of the transmitter identification to different topologies and different Kasami sequence lengths, we present the analysis here for four different geometric layouts, namely circular distribution, doubly concentric and circular distribution, square array and hexagonal tessellation under the ENG (electronic news gathering) crews¿ working environment. The coverage area and the lowest received signal-to-interference ratio are considered as two essential parameters for the multiple-transmitter identification. It turns out to be that the larger the Kasami sequence length, the larger the received signal- to-interference ratio. Our new analysis can be used to determine the required Kasami sequence length for a specific broadcasting coverage.

A New Positioning System Using DVB-T2 Transmitter Signature Waveforms in Single Frequency Networks

A new positioning system using transmitter signature waveforms of DVB-T2 in Single Frequency Networks (SFNs) is proposed in this paper. Due to the wide coverage of the SFN and extremely high transmission power of digital television (DTV) transmitting stations, positioning based on DTV SFN can be considered as a promising complementary to remedy the poor coverage of the Global Positioning System (GPS) in dense multipath propagation environments. Based on the newly proposed transmitter signature waveform in DVB-T2 systems, transmitter identification and timing estimation using threshold-based first path detection method are jointly achieved, and subsequently utilized for Time-Difference-of-Arrival (TDOA)-based positioning approach. The proposed positioning system is fully compatible with the current DVB-T2 standard and therefore, no dedicated hardware modification is required. The performance of the proposed positioning system is evaluated through computer simulations under different signal propagation scenarios and compared with existing methods. Simulation results show that very high accuracy can be obtained with the proposed positioning system.

Individual Communication Transmitter Identification using SVMs with Polyspectrum Kernels of Average Carrier Frequency

Individual Communication Transmitter Identification using SVMs with Polyspectrum Kernels of Average Carrier Frequency