Transmitter Identification Research Articles

Features and Applications of ATSC 3.0 Transmitter Identification (TxID)

Features and Applications of ATSC 3.0 Transmitter Identification (TxID)

Optical transmitter fingerprint construction and identification based on chaotic phase space reconfiguration.

An optical transmitter identification scheme based on optical chaotic phase space reconfiguration for secure communication is proposed to target injection attacks in the physical layer of optical networks. First, a feature fingerprint construction method based on reconfigured phase space of optical chaos is proposed. Then the fingerprint is controlled by the feedback intensity and filtering bandwidth of chaos. The in-phase and quadrature-phase encryption (IQE)/decryption (IQD) ensures the loading of fingerprints and realizes the confidential communication. In the experiment, the recognition rate of three transmitters is up to 99.3%. In the simulation, the recognition rate of five optical transmitters reaches 100% after 600 km transmission. The bit error rate of 25 GBaud QPSK signal after 300 km transmission at 25 dB OSNR is 1.6 × 10-3. Compared with the traditional optical transmitter identification methods, the fingerprint of this scheme is controllable. The IQE and IQD not only realize the chaotic fingerprint loading but also ensure the secure transmission of the signal avoiding the synchronization and time delay exposure problems in traditional chaotic communication systems. It is robust to device parameters, with low implementation difficulty and low cost. Therefore, this scheme has research and application value for secure communication in the physical layer of optical networks.

A practical radio frequency fingerprinting scheme for mobile phones identification

Radio frequency fingerprint (RFF) has attracted a remarkable surge of attention to wireless transmitter identification due to the inimitable hardware imperfection. However, most RFF-based schemes devised in controlled environments are unable to reach the claimed performance in real-world scenarios. This paper proposes a practical RFF identification method for mobile phones in Global System for Mobile (GSM) system. Specifically, the instantaneous amplitude of near-transient part in normal burst (NB) is regarded as RFF that extracted from up-link communications. In addition, an anomaly filtering and stacking (AFS) method is introduced to obtain stable RFF in Time Division Multiple Access (TDMA) system. Lastly, the impacts of frequency point and sending power variations on RFF are fully dissected in comparison experiments. Experiments on 10 mobile phones show that the proposed RFF scheme yields 99.17% True Acceptance Rate (TAR) in real wireless environments. Experiments also show that the varying transmission power decreases the accuracy of RFF identification.

New probabilistic SINR analysis for capacity and reception-quality studies of DTV transmitter identification systems

New probabilistic SINR analysis for capacity and reception-quality studies of DTV transmitter identification systems

RF Transmitter Identification Using Combined Siamese Networks

RF transmitter identification is facing a big challenge since the increasing use of wireless devices in recent years. Traditional methods for identification are implemented by choosing specific rapid fingerprints manually, which fails to distinguish threats from unknown or rogue transmitters in a few shots under the complex electromagnetic environment. In order to solve this problem, a combined Siamese networks learning method for RF transmitter identification (CSNTI) is proposed in this work by considering both the data augmentation and the classical fingerprints of RF signals. The proposed method is composed of a series of classifiers trained by Siamese networks. Each classifier is utilized to distinguish one transmitter from others. Based on the special structure of Siamese networks, the number of training samples for each classifier increases greatly, which is efficient for data augmentation and takes full use of the limited RF transmitter signals. Then, the softmax procedure is followed to normalize the output of all classifiers. A criterion for unknown transmitters’ identification is proposed. Numerical experiments for basic classification and new devices identification with eight known transmitters and three unknown transmitters are validated. Results of compared methods suggest that the highest accuracy across a broad range of conditions is achieved by the proposed method.

WiSig: A Large-Scale WiFi Signal Dataset for Receiver and Channel Agnostic RF Fingerprinting

RF fingerprinting leverages circuit-level variability of transmitters to identify them using signals they send. Signals used for identification are impacted by a wireless channel and receiver circuitry, creating additional impairments that can confuse transmitter identification. Eliminating these impairments or just evaluating them, requires data captured over a prolonged period of time, using many spatially separated transmitters and receivers. In this paper, we present WiSig; a large-scale WiFi dataset containing 10 million packets captured from 174 off-the-shelf WiFi transmitters and 41 USRP receivers over 4 captures spanning a month. WiSig is publicly available, not just as raw captures, but as conveniently pre-processed subsets of limited size, along with the scripts and examples. A preliminary evaluation performed using WiSig shows that changing receivers, or using signals captured on a different day can significantly degrade a trained classifier’s performance. While capturing data over more days or more receivers limits the degradation, it is not always feasible, and novel data-driven approaches are needed. WiSig provides the data to develop and evaluate these approaches towards channel and receiver agnostic transmitter fingerprinting.

MsmcNet: A Modular Few-Shot Learning Framework for Signal Modulation Classification

With the development of deep learning (DL) technology, the feature extraction capability of networks has gradually been enhanced, and high accuracy has been achieved in signal modulation classification (SMC) tasks. DL requires numerous training samples to achieve high classification accuracy. However, in non-cooperative cases, only a few labeled data are usually available. To solve this problem, we propose a modular few-shot learning framework for SMC, called MsmcNet. MsmcNet comprises an IQ fusion (IQF) module, 1D signal feature processing (1D-SFP) modules, and a classifier. The number of the 1D-SFP modules is variable and is determined by a GCN-based method. Experimental results on RadioML2016.04c and RadioML2016.10a show that the proposed approach outperforms the baselines in all aspects. In particular, for the RadioML2016.10a dataset, when the SNR is 6 dB and the training data account for more than 2% of the test data, the classification accuracy of MsmcNet is greater than 70%. To further meet the requirements in the non-cooperative case, we explore the performance of the proposed MsmcNet in the one-shot learning case and find that it maintains its high performance advantage. In addition to the SMC task, we explore the performance of MsmcNet in the transmitter identification task to verify the universality of the framework. Compared to the baselines, MsmcNet achieves outstanding results.

Radio frequency fingerprint identification based on stream differential constellation trace figures

Recently, radio frequency fingerprint (RFF) technique, characterized by unique hardware impairments, has seen vast attention for specific transmitter identification and authorized access. Differential constellation trace figure (DCTF) emerges as an effective image-based RFF method, which forms a stable constellation figure by performing a key differential process. Previous studies that are based on a single differential interval (DI) are susceptible to noise, which leads to under-performance in cases with low signal-to-noise ratios (SNRs). To address this problem, this paper proposes a stream DCTFs scheme that mainly consists of (i) RFF extraction deriving from numerous DIs, (ii) devising a feature reduction method containing feature importance ranking (FR) and feature set selection (FS), and (iii) extensive experimental evaluations. When the SNR ranges from 5 dB up to 10 dB, the classification results reach 96.3% and 98.4% respectively among 54 ZigBee devices, which are about 40% and 20% higher than existing DCTF-based methods. Moreover, the authentication accuracy reaches 100% in verification.

New algebraic studies of pattern attributes in maximum-length shift-register sequences

In order to facilitate the ubiquitous heterogeneous and medium-independent communication networks, transmitter identification is crucial especially as the number of transmitters becomes tremendous in reality. Since the birth of spread-spectrum communications, pseudo-random identification sequences have been widely adopted due to their preferable nearly Dirac-Delta auto-correlation property, which would lead to the advantages in synchronization and multi-user interference mitigation. Maximum-length shift-register sequences (a.k.a. m-sequences) are pseudo-random sequences often adopted for multi-access communications, but categorization or grouping of the patterns (bit substrings) contained in m-sequences has not been investigated to the best of our knowledge. Thus, this paper is dedicated to the new study on the categorization of m-sequences, which would give rise to many applications involving assignment, addressing, and management of identification or spreading sequences. We define new essential parameters, namely single-pattern-searching parameters, and design a new parallel algorithm to spot such inherent parameters associated with each selected underlying pattern. Our proposed new approach will facilitate a highly computationally-efficient solution to find the pattern-attributed (pattern-contained) m-sequences without actually generating any m-sequence for subsequence-matching between an m-sequence and the underlying pattern(s). We further utilize these single-pattern-searching parameters to establish the new analysis of pattern capacity and quality by characterizing multiple pattern attributes within m-sequences. The first goal of this work is to find single-pattern-attributed m-sequences recursively until the final subset of m-sequences is acquired so that each m-sequence contains all underlying patterns. The second goal of this work is to find the shortest sequences containing specified patterns. To achieve this goal, we first construct the shortest binary sequences subject to the underlying patterns and it can be accomplished by solving the generalized traveling salesman problem (GTSP). Then, according to the constructed shortest binary sequences, the number of pattern-contained m-sequences can be determined thereby. Memory- and computational-complexity analyses are also presented to demonstrate that our proposed new scheme is much more computationally efficient than the conventional subsequence-matching method for searching and counting the pattern-attributed m-sequences. On the other hand, our proposed new scheme leads to the same memory-complexity as the conventional method for registering the spotted pattern-attributed m-sequences. Finally, three new metrics for assessing the underlying patterns are proposed. They are attributability, discriminability, and ambiguity. The associated numerical evaluation is also provided in this paper.

Emitter Identification of Digital Modulation Transmitter Based on Nonlinearity and Modulation Distortion of Power Amplifier.

Specific transmitter identification (SEI) is a technology that uses a received signal to identify to which individual radiation source the transmitted signal belongs. It can complete the identification of the signal transmitter in a non-cooperative scenario. Therefore, there are broad application prospects in the field of wireless-communication-network security, spectral resource management, and military battlefield-target communication countermeasures. This article demodulates and reconstructs a digital modulation signal to obtain a signal without modulator distortion and power-amplifier nonlinearity. Comparing the reconstructed signal with the actual received signal, the coefficient representation of the nonlinearity of the power amplifier and the distortion of the modulator can be obtained, and these coefficients can be used as the fingerprint characteristics of different transmitters through a convolutional neural network (CNN) to complete the identification of specific transmitters. The existing SEI strategy for changing the modulation parameters of a test signal is to mix part of the test signal with the training signal so that the classifier can learn the signal of which the modulation parameter was changed. This method is still data-oriented and cannot process signals for which the classifier has not been trained. It has certain limitations in practical applications. We compared the fingerprint features extracted by the method in this study with the fingerprint features extracted by the bispectral method. When SNR < 20 dB, the recognition accuracy of the bispectral method dropped rapidly. The method in this paper still achieved 86% recognition accuracy when SNR = 0 dB. When the carrier frequency of the test signal was changed, the bispectral feature failed, and the proposed method could still achieve a recognition accuracy of about 70%. When changing the test-signal baud rate, the proposed method could still achieve a classification accuracy rate of more than 70% for four different individual radiation sources when SNR = 0 dB.

Deep cascading network architecture for robust automatic modulation classification

BACKGROUND: Automatic modulation classification (AMC) plays a crucial role in cognitive radio, such as industrial automation, transmitter identification, and spectrum resource allocation. Recently, deep learning (DL) as a new machine learning (ML) methodology has achieved considerable implementation in AMC missions. However, few studies have examined the robustness of DL models under varying signal-to-noise ratio (SNR) environments.OBJECTIVE: The primary objective of this paper is to design a robust DL-based AMC model to adapt to noise changes.METHODS: The AMC task is divided into two sub-problems: SNR environment perception and modulation classification in sub-environments. A deep cascading network architecture (DCNA) is proposed to solve these two problems. DCNA is composed of an SNR estimator network (SEN) and a modulation recognition cluster network (MRCN). SEN is designed to identify the SNR levels of samples, and MRCN is composed of several subnetworks for further modulation recognition under diverse SNR settings. In addition, a label-smoothing method is proposed to promote the integration between SEN and MRCN. An auxiliary data-segmenting method is also presented to deal with the contrasting data requirements of DCNA. Note that DCNA does not utilize a specific network structure and can be generalized to various deep learning models with advanced improvements.RESULTS: Experimental results on dataset RML2016.10b show that our proposed DCNA can enhance the recognition performance of different network structures on AMC tasks. In particular, a combination of DCNA and convolutional long short-term deep neural network (CLDNN) can achieve a classification accuracy of 91.0%, outperforming the previous research.CONCLUSION: The performance of the cascading network demonstrates the significant performance advantage and application feasibility of DCNA.

Exploiting Spatio-Temporal Properties of I/Q Signal Data Using 3D Convolution for RF Transmitter Identification

In this work we implement an automatic emitter identification system using three dimensional convolutional neural networks (3D-CNN). We show that it is possible to leverage the short term spatio-temporal properties of the raw I/Q signal data using 3D-CNN and apply this to the task of transmitter identification. The time series of raw I/Q signal data exhibits a “helical” structure which encodes the intrinsic properties of the emitter as short term variations. These features are exploited by our system to create unique “fingerprints” for the emitters, which are finally used for the task of identification. Our system is end-to-end, works with the raw I/Q signal data and is able to automatically discriminate between different transmitters without using a recurrent structure, thus reducing the complexity of the system. We experimented with four novel RF transmitter datasets that we curated in an uncalibrated and uncontrolled indoor environment. The first dataset consists of four identical transmitters, the second eight identical transmitters, the third four heterogeneous transmitters and the last eight heterogeneous transmitters. We use Ettus Research USRP Software Defined Radios (USRP B210, B200, X310), ADLM Pluto and BladeRF for transmission and collect the raw signal data using a RTL Software Defined Radio (RTL SDR) receiver dongle, from each of the four and eight emitters at a time. We use the data to train and test the 3D convolutional neural network for discerning intrinsic transmitter characteristics for the task of classification. Through our methods and implementation, we show that we can identify the transmitters with ~99 % accuracy.

Securing the Insecure: A First-Line-of-Defense for Body-Centric Nanoscale Communication Systems Operating in THz Band.

This manuscript presents a novel mechanism (at the physical layer) for authentication and transmitter identification in a body-centric nanoscale communication system operating in the terahertz (THz) band. The unique characteristics of the propagation medium in the THz band renders the existing techniques (say for impersonation detection in cellular networks) not applicable. In this work, we considered a body-centric network with multiple on-body nano-senor nodes (of which some nano-sensors have been compromised) who communicate their sensed data to a nearby gateway node. We proposed to protect the transmissions on the link between the legitimate nano-sensor nodes and the gateway by exploiting the path loss of the THz propagation medium as the fingerprint/feature of the sender node to carry out authentication at the gateway. Specifically, we proposed a two-step hypothesis testing mechanism at the gateway to counter the impersonation (false data injection) attacks by malicious nano-sensors. To this end, we computed the path loss of the THz link under consideration using the high-resolution transmission molecular absorption (HITRAN) database. Furthermore, to refine the outcome of the two-step hypothesis testing device, we modeled the impersonation attack detection problem as a hidden Markov model (HMM), which was then solved by the classical Viterbi algorithm. As a bye-product of the authentication problem, we performed transmitter identification (when the two-step hypothesis testing device decides no impersonation) using (i) the maximum likelihood (ML) method and (ii) the Gaussian mixture model (GMM), whose parameters are learned via the expectation–maximization algorithm. Our simulation results showed that the two error probabilities (missed detection and false alarm) were decreasing functions of the signal-to-noise ratio (SNR). Specifically, at an SNR of 10 dB with a pre-specified false alarm rate of , the probability of correct detection was almost one. We further noticed that the HMM method outperformed the two-step hypothesis testing method at low SNRs (e.g., a increase in accuracy was recorded at SNR = −5 dB), as expected. Finally, it was observed that the GMM method was useful when the ground truths (the true path loss values for all the legitimate THz links) were noisy.

Bayesian Neural Networks for Identification and Classification of Radio Frequency Transmitters Using Power Amplifiers’ Nonlinearity Signatures

The edge devices in an emerging Internet-of-Things (IoT) environment require comprehensive security measures that are within the power budget for ubiquitous computing. In this paper, a transmitter identification scheme consisting of a lightweight Bayesian neural network (BNN)-based classifier using raw time-domain data is presented. Evaluation is performed with data obtained in schematic-level simulation of high-efficiency CMOS power amplifier designs using a 65 nm process design kit (PDK). The Bayesian neural networks achieve 89.5% accuracy on the task of classifying six transmitters. Moreover, the BNN classifier is implemented on field-programmable gate array (FPGA) with parallel pseudo-Gaussian random number generators to achieve a throughput of more than 340,000 classifications per second, with average energy consumption for each classification task of 0.548 μJ. This low-power system enables comprehensive security for energy-constrained IoT devices and sensors.

Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations

Due to imperfections in transmitters' hardware, wireless signals can be used to verify their identity in an authorization system. While deep learning was proposed for transmitter identification, existing work has mainly focused on classification among a closed set of transmitters. Malicious transmitters outside this closed set will be misclassified, jeopardizing the authorization system. In this article, we formulate the problem of recognizing authorized transmitters and rejecting new transmitters as open set recognition and anomaly detection. We consider approaches based on one and several binary classifiers, multiclass classifiers, and signal reconstruction. We study how these approaches scale with the required number of authorized transmitters. We propose using a known set of unauthorized transmitters to assist the training and study its impact. The evaluation procedure takes into consideration that some transmitters might be more similar than others and nuances these effects. The authorization's robustness against temporal changes in fingerprints is also evaluated as a function of the approach and the dataset structure. When using 10 authorized and 50 known unauthorized WiFi transmitters from a publicly accessible testbed, we were able to achieve an outlier detection accuracy of 98% on the same day test set and 80% on the different day test set.

Machine Learning-Based Massive Augmented Spatial Modulation (ASM) for IoT VLC Systems

Massive Multiple-Input Multiple-Output (MIMO) technology aims to further the diversity/multiplexing gains of wireless communication systems. Spatial modulation (SM) is a renowned low-complexity MIMO scheme that jointly uses transmitting source indices along with the data stream to convey information. However, the reliability of the spatial stream in SM is significantly influenced by the correlation between channel coefficients. Hence, applying massive-MIMO in visible light communications (VLC) remains an under-investigated area of research, due to the ill-conditioned massive-MIMO VLC channel. Augmented SM (ASM) is an approach that overcomes the channel uniqueness requirement for SM-based VLC systems. This letter adopts massive-ASM for Internet-of-Things applications with a focus on investigating ASM’s complexity and introducing different machine learning based receiver designs, including; support vector machine (SVM), logistic regression (LR), and a neural network (NN). The computational time and transmitter identification accuracy are compared and the system’s bit-error-rate performance is evaluated.

Transmitter Identification Signal Detection Algorithm for ATSC 3.0 Single Frequency Networks

To effectively operate and maintain an existing single frequency network (SFN), the advanced television systems committee (ATSC) 3.0 transmission system has utilized a transmitter identification (TxID) technique. The TxID technique aims is to obtain the channel impulse response (CIR) of each transmitter independently to support the adjustment of the relative power level and delay offset of individual transmitters. The professional receiver can detect such CIR, but a commercial ATSC 3.0 receiver does not need to be necessarily decoded the TxID signal. Also, according to the ATSC 3.0 standard, the TxID signal is embedded into the preamble signal with an appropriately low injection level (IL), such that the impact on the preamble reception is negligible. This paper proposes an efficient TxID signal detection algorithm to enhance the individual CIR detection performance during the TxID analyzing process. The simulation results indicate that the proposed TxID signal detection algorithm can offer better individual CIR detection performance as compared to the conventional TxID signal detection algorithms, even though the IL of TxID signal is low.

Cooperative Topology Sensing of Wireless Networks With Distributed Sensors

As a key technology of network inference, topology sensing plays an important role in understanding, explaining and predicting network behavior. Majority of existing studies focus on topology inference with complete and perfect observations, which is mainly suitable for wired networks rather than wireless networks with kinds of channel randomness and uncertainty. This article investigates the issue of topology sensing of wireless networks with distributed sensors to handle the situation of limited and unreliable information, which include a time series of the signal presence instants and the corresponding transmitter identification. Firstly, a cooperative topology sensing framework is developed to exploit multi-sensor spatial diversity for coping with wireless randomness. Then, based on the observation that in wireless networks, events at one node are likely to induce a response at some other node in the form of for example an acknowledgement message or a forwarding action, we formulate the communication events among multiple wireless transceivers as a multi-dimensional Hawkes process. Further, we propose and analyze four cooperative topology sensing schemes with distributed sensors from the perspective of hard fusion and soft fusion, respectively. Moreover, simulation results show that the signal power and the event number have a great influence on sensing accuracy and cooperative topology sensing is an effective measure to deal with the issues of wireless channel randomness.

Exploiting Channel Distortion for Transmitter Identification for In-Vehicle Network Security

Exploiting Channel Distortion for Transmitter Identification for In-Vehicle Network Security

Time delay estimation issues for target detection and transmitter identification in multistatic radars

AbstractTime delay estimation (TDE) is an active research area due to its importance in a wide range of applications, such as target detection and transmitter identification. At present, the majority of the radar systems are using multistatic architecture because of its improved detection and tracking performance. In multistatic radar systems, where more than one transmitter and receivers are deployed, it is a challenging task to separate multiple echoes at the receiver to estimate the required parameters and identify the number of transmitters. The delay estimation process becomes more complicated when closely spaced multiple targets are involved in the detection process. In this study, various issues associated with conventional matched filtering‐based target detection techniques are investigated in terms of nonlinear time‐frequency representation with ambiguity function and cross‐ambiguity functions. The problems of delay estimation and cross‐range resolution are analyzed through simulations in a multistatic radar system.