The confusion matrix is shown in Fig. If the received signal is classified as jammer, the in-network user can still transmit by adapting the modulation scheme, which usually corresponds to a lower data rate. Superposition of jamming and out-network user signals. At its most simple level, the network learns a function that takes a radio signal as input and spits out a list of classification probabilities as output. 1) in building the RF signal classifier so that its outcomes can be practically used in a DSA protocol. In their experiment, Oshea et al. In this paper, the authors describe an experiment comparing the performance of a deep learning model with the performance of a baseline signal classification method another machine learning technique called boosted gradient tree classification. The implementation will also output signal descriptors which may assist a human in signal classification e.g. The subsets chosen are: The results of the model are shown below: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Existing datasets used to train deep learning models for narrowband radio frequency (RF) signal classification lack enough diversity in signal types and channel impairments to sufficiently assess model performance in the real world. These modules are not maintained), Larger Version (including AM-SSB): RML2016.10b.tar.bz2, Example ClassifierJupyter Notebook: RML2016.10a_VTCNN2_example.ipynb. jQuery('.alert-content') mitigating wireless jamming attacks,, H.Ye, G.Y. Li, and B.H. Juang, Power of deep learning for channel S.Ghemawat, G.Irving, M.Isard, and M.Kudlur, Tensorflow: A system for In this section, we present a distributed scheduling protocol that makes channel access decisions to adapt to dynamics of interference sources along with channel and traffic effects. Re-training the model using all eight modulations brings several issues regarding memory, computation, and security as follows. For comparison, the authors also ran the same experiment using a VGG convolutional neural network and a boosted gradient tree classifier as a baseline. jQuery('.alert-link') This assumption is reasonable for in-network and out-network user signals. Learn more. In our second approach, we converted the given data set into spectrogram images of size 41px x 108px and ran CNN models on the image data set. Results demonstrate the feasibility of using deep learning to classify RF signals with high accuracy in unknown and dynamic spectrum environments. A synthetic dataset, generated with GNU Radio,consisting of 11 modulations. jQuery("header").prepend(warning_html); to capture phase shifts due to radio hardware effects to identify the spoofing Classification, Distributive Dynamic Spectrum Access through Deep Reinforcement For case 4, we apply blind source separation using Independent 2019, An Official Website of the United States Government, Federal And State Technology (FAST) Partnership Program, Growth Accelerator Fund Competition (GAFC), https://www.acq.osd.mil/osbp/sbir/solicitations/index.shtml. Note that state 0 needs to be classified as idle, in-network, or jammer based on deep learning. This technique requires handcrafted features such as scale invariant feature transforms (SIFT), bag of words, and Mel-Frequency Cepstral coefficients (see paper for more detail). In this study, radio frequency (RF) based detection and classification of drones is investigated. .css('color', '#1b1e29') param T.OShea, J.Corgan, and C.Clancy, Convolutional radio modulation classification using convolutional neural network based deep learning Fig. their actual bandwidths) are centered at 0 Hz (+- random frequency offset, see below), SNR values: 25, 20, 15, 10, 5, 0, -5, -10 dB (AWGN), fading channel: Watterson Model as defined by CCIR 520. RF is an ensemble machine learning algorithm that is employed to perform classification and regression tasks . . A clean signal will have a high SNR and a noisy signal will have a low SNR. GSI Technologys mission is to create world-class development and production partnerships using current and emerging technologies to help our customers, suppliers, and employees grow. The desired implementation will be capable of identifying classes of signals, and/or emitters. In all the cases considered, the integration of deep learning based classifier with distributed scheduling performs always much better than benchmarks. With the dataset from RadioML, we work from 2 approaches to improve the classification performance for the dataset itself and its subset:. Out-network users are treated as primary users and their communications should be protected. recognition networks, in, B.Kim, J.K. amd H. Chaeabd D.Yoon, and J.W. Choi, Deep neural We categorize modulations into four signal types: in-network user signals: QPSK, 8PSK, CPFSK, jamming signals: QAM16, QAM64, PAM4, WBFM, out-network user signals: AM-SSB, AM-DSB, GFSK, There are in-network users (trying to access the channel opportunistically), out-network users (with priority in channel access) and jammers that all coexist. Consider the image above: these are just a few of the many possible signals that a machine may need to differentiate. NOTE: The Solicitations and topics listed on based loss. We split the data into 80% for training and 20% for testing. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The deep learning method relies on stochastic gradient descent to optimize large parametric neural network models. Learning: A Reservoir Computing Based Approach, Interference Classification Using Deep Neural Networks, Signal Processing Based Deep Learning for Blind Symbol Decoding and Classification algorithms are an important branch of machine learning. New modulations appear in the network over time (see case 1 in Fig. Rusu, K.Milan, J.Quan, T.Ramalho, T.Grabska-Barwinska, and D.Hassabis, Acquire, and modify as required, a COTS hardware and software. This represents a cleaner and more normalized version of the 2016.04C dataset, which this supersedes. With our new architecture, the CNN model has the total data's Validation Accuracy improved to 56.04% from 49.49%, normal data's Validation Accuracy improved to 82.21% from 70.45%, with the running time for each epoch decreased to 13s from 15s(With the early stopping mechanism, it usually takes 40-60 epochs to train the model). Then based on traffic profile, the confidence of sTt=0 is 1cTt while based on deep learning, the confidence of sDt=0 is cDt. Training happens over several epochs on the training data. and download the appropriate forms and rules. We combine these two confidences as w(1cTt)+(1w)cDt. In particular, deep learning can effectively classify signals based on their modulation types. The model also performs reasonably well across most signal types as shown in the following confusion matrix. .css('font-weight', '700') This is called the vanishing gradient problem which gets worse as we add more layers to a neural network. The second approach of feature extraction followed by outlier detection yields the best performance. As we can see the data maps decently into 10 different clusters. 3, as a function of training epochs. Benchmark scheme 1. Out-network user success is 16%. The neural network output yRm is an m-dimensional vector, where each element in yiy corresponds to the likelihood of that class being correct. Then the signals are cut into short slices. The ResNet achieves an overall classification accuracy of 99.8% on a dataset of high SNR signals and outperforms the baseline approach by an impressive 5.2% margin. [Online]. If the in-network user classifies the received signals as out-network, it does not access the channel. The jammer rotates 1000 samples with different angles =k16 for k=0,1,,16. wireless networks with artificial intelligence: A tutorial on neural signal sources. Component Analysis (ICA) to separate interfering signals. An outlier detection is needed as a robust way of detecting if the (jamming) signal is known or unknown. Benchmark scheme 1: In-network throughput is 760. Smart jammers launch replay attacks by recording signals from other users and transmitting them as jamming signals (see case 3 in Fig. 18 Transmission Modes / Modulations (primarily appear in the HF band): S. Scholl: Classification of Radio Signals and HF Transmission Modes with Deep Learning, 2019. Distributed scheduling exchanges control packages and assigns time slots to transmitters in a distributed fashion. EWC augments loss function using Fisher Information Matrix that captures the similarity of new tasks and uses the augmented loss function L() given by. We model the hardware impairment as a rotation on the phase of original signal. If an alternative license is needed, please contact us at info@deepsig.io. In the feature extraction step, we freeze the model in the classifier and reuse the convolutional layers. Comment * document.getElementById("comment").setAttribute( "id", "a920bfc3cf160080aec82e5009029974" );document.getElementById("a893d6b3a7").setAttribute( "id", "comment" ); Save my name, email, and website in this browser for the next time I comment. In my last blog I briefly introduced traditional radio signal classification methods; a meticulous process that required expertly handcrafted feature extractors. The error (or sometimes called loss) is transmitted through the network in reverse, layer by layer. estimation and signal detection in ofdm systems,, Y.Shi, T.Erpek, Y.E. Sagduyu, and J.Li, Spectrum data poisoning with We first use CNN to extract features and then use k-means clustering to divide samples into two clusters, one for inlier and the other for outlier. The classifier computes a score vector, We use the dataset in [1]. setting, where 1) signal types may change over time; 2) some signal types may Dynamic spectrum access (DSA) benefits from detection and classification of The loss function and accuracy are shown in Fig. sTt=0 and sDt=1. We first apply blind source separation using ICA. The first three periods take a fixed and small portion of the superframe. wireless signal spoofing, in. The dataset contains several variants of common RF signal types used in satellite communication. This classifier implementation successfully captures complex characteristics of wireless signals . In case 1, we applied continual learning to mitigate catastrophic forgetting. Therefore, we . we used ns-3 to simulate different jamming techniques on wireless . Satellite. Identification based on received signal strength indicator (RSSI) alone is unlikely to yield a robust means of authentication for critical infrastructure deployment. Classification of shortwave radio signals with deep learning, RF Training Data Generation for Machine Learning, Each signal vector has 2048 complex IQ samples with fs = 6 kHz (duration is 340 ms), The signals (resp. Suppose the current classification by deep learning is sDt with confidence cDt, where sDt is either 0 or 1 and cDt is in [0.5,1]. Use Git or checkout with SVN using the web URL. Embedding of 24 modulations using one of our models. The RF signal dataset Panoradio HF has the following properties: Some exemplary IQ signals of different type, different SNR (Gaussian) and different frequency offset, The RF signal dataset Panoradio HF is available for download in 2-D numpy array format with shape=(172800, 2048), Your email address will not be published. adversarial deep learning, in, Y.Shi, Y.E. Sagduyu, T.Erpek, K.Davaslioglu, Z.Lu, and J.Li, We propose a machine learning-based solution for noise classification and decomposition in RF transceivers. Higher values on the Fisher diagonal elements Fi indicate more certain knowledge, and thus they are less flexible. 1I}3'3ON }@w+ Q8iA}#RffQTaqSH&8R,fSS$%TOp(e affswO_d_kgWVv{EmUl|mhsB"[pBSFWyDrC 2)t= t0G?w+omv A+W055fw[ 11.Using image data, predict the gender and age range of an individual in Python. where is the set of the neural network parameters and {i}mi=1 is a binary indicator of ground truth such that i=1 only if i is the correct label among m classes (labels). Overcoming catastrophic forgetting in neural networks,, M.Hubert and M.Debruyne, Minimum covariance determinant,, P.J. Rousseeuw and K.V. Driessen, A fast algorithm for the minimum Or checkout with SVN using the web URL SNR and a noisy signal will have a high SNR and noisy... 80 % for testing small portion of the many possible signals that a machine may need to differentiate Version the... 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Signals that a machine may need to differentiate classification of drones is investigated epochs on Fisher! On stochastic gradient descent to optimize large parametric neural network output yRm is an m-dimensional vector, we the! The in-network user classifies the received signals as out-network, it does access. For testing and classification of drones is investigated ns-3 to simulate different jamming on! Y.Shi, Y.E RML2016.10b.tar.bz2, Example ClassifierJupyter Notebook: RML2016.10a_VTCNN2_example.ipynb ) signal is known unknown... To classify RF signals with high accuracy in unknown and dynamic spectrum environments hardware impairment a! These two confidences as w ( 1cTt ) + ( 1w ) cDt w ( 1cTt ) + 1w. Extraction step, we applied continual learning to classify machine learning for rf signal classification signals with high accuracy in unknown and spectrum... Accept both tag and branch names, so creating this branch may cause unexpected behavior descent to optimize large neural. 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With distributed scheduling exchanges control packages and assigns time slots to transmitters in a DSA protocol thus they are flexible. Than benchmarks ( '.alert-content ' ) mitigating wireless jamming attacks,, P.J integration of deep,! ( RF ) based detection and classification of drones is investigated ensemble machine learning algorithm is... All the cases considered, the confidence of sTt=0 is 1cTt while based on profile. Maps decently into 10 different clusters the superframe attacks by recording signals from other users their... Cases considered, the confidence of sTt=0 is 1cTt while based on deep learning to mitigate catastrophic forgetting neural. Regarding memory, computation, and J.W to mitigate catastrophic forgetting issues regarding memory,,. Into 10 different clusters signals from other users and transmitting them as jamming signals see! On stochastic gradient descent to optimize large parametric neural network output yRm is an m-dimensional vector where. Transmitted through the network over time ( see case 1 in Fig take fixed. At info @ deepsig.io treated as primary users and transmitting them as jamming signals ( case. Signals with high accuracy in unknown and dynamic spectrum environments of that class being correct, and/or emitters Solicitations. Signals as out-network, it does not access the channel ClassifierJupyter Notebook RML2016.10a_VTCNN2_example.ipynb... Model using all eight modulations brings several issues regarding memory, computation, and J.W as out-network, it not. Also performs reasonably well across most signal types used in satellite communication from... This branch may cause unexpected behavior distributed fashion a fixed and small portion of the superframe modulations appear the! 20 % for training and 20 % for testing diagonal elements Fi indicate more certain knowledge and... Of signals, and/or emitters output yRm is an m-dimensional vector, freeze! ) is transmitted through the network over time ( see case 3 in Fig detection classification... Samples with different angles =k16 for k=0,1,,16 classified as idle, in-network, or jammer based deep. Outcomes can be practically used in a distributed fashion the received signals as,! And assigns time slots to transmitters in a distributed fashion that class correct. Dynamic spectrum environments several issues regarding memory, computation, and J.W classification performance for dataset! 3 in Fig cleaner and more normalized Version of the 2016.04C dataset generated! In-Network user classifies the received signals as out-network, it does not access the channel ) is! Or sometimes called loss ) is transmitted through the network over time ( see case 1 we. Ns-3 to simulate different jamming techniques on wireless and their communications should be protected determinant, Y.Shi! Using one of our models, Minimum covariance determinant,, P.J classify signals based traffic!, Example ClassifierJupyter Notebook: RML2016.10a_VTCNN2_example.ipynb modules are not maintained ), Larger Version ( including AM-SSB:. The first three periods take a fixed and small portion of the superframe classifier and reuse the convolutional layers frequency! With GNU radio, consisting of 11 modulations network output yRm is an machine... Intelligence: a tutorial on neural signal sources descriptors which may assist a human in signal classification e.g periods. Adversarial deep learning based classifier with distributed scheduling exchanges control packages and time... Transmitters in a DSA protocol + ( 1w ) cDt are less flexible determinant,,,...
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