machine learning for rf signal classification

In this blog I will give a brief overview of the research paper Over the Air Deep Learning Based Signal Classification. Benchmark scheme 2. .admin-menu.alert-message { padding-top:25px !important;} As the name indicates, it is comprised of a number of decision trees. Signal Generation Software: https://github.com/radioML/dataset Warning! signals are superimposed due to the interference effects from concurrent transmissions of different signal types. decisions and share the spectrum with each other while avoiding interference You signed in with another tab or window. Signal to noise ratio (or SNR) is the ratio of the signal strength containing desired information to that of the interference. Adversarial deep learning for cognitive radio security: Jamming attack and We have the following benchmark performance. In the past few years deep learning models have out-paced traditional methods in computer vision that, like the current state of signal classification, involved meticulously creating hand-crafted feature extractors. PHASE I:Identify/generate necessary training data sets for detection and classification of signatures, the approach may include use of simulation to train a machine learning algorithm. In Fig. Rukshan Pramoditha. In the training step of MCD classifier, we only present the training set of known signals (in-network and out-network user signals), while in the validation step, we test the inlier detection accuracy with the test set of inliers and test the outlier detection accuracy with the outlier set (jamming signals). Please For example, radio-frequency interference (RFI) is a major problem in radio astronomy. The goal is to improve both measures. In particular, deep learning has been applied to learn complex spectrum environments, including spectrum sensing by a CNN [15], spectrum data augmentation by generative adversarial network (GAN) [16, 17], , channel estimation by a feedforward neural network (FNN). At each SNR, there are 1000samples from each modulation type. However, jamming signals are possibly of an unknown type (outlier). Benchmark performance is the same as before, since it does not depend on classification: The performance with outliers and signal superposition included is shown in TableVII. 11. .css('color', '#1b1e29') A superframe has 10 time slots for data transmission. PHASE III:Integration of the detection and classification system into Next Generation Combat Vehicles (NGCV) as well as current vehicles such as the Stryker, the Bradley and the Abrams. In this study, computer-aided diagnosis (CAD) systems were used to improve the image quality of mammography images and to detect suspicious areas. Classification, Distributive Dynamic Spectrum Access through Deep Reinforcement Smart jammers launch replay attacks by recording signals from other users and transmitting them as jamming signals (see case 3 in Fig. The paper proposes using a residual neural network (ResNet) to overcome the vanishing gradient problem. Dataset Download: 2018.01.OSC.0001_1024x2M.h5.tar.gz Cognitive Radio Applications of Machine Learning Based RF Signal Processing AFCEA Army Signal Conference, March 2018 MACHINE LEARNING BENEFITS 6 Applicable to diverse use cases including Air/Ground integration, Army expeditionary In my last blog I briefly introduced traditional radio signal classification methods; a meticulous process that required expertly handcrafted feature extractors. Recent advances in machine learning (ML) may be applicable to this problem space. The architecture contains many convolutional layers (embedded in the residual stack module). The ResNet model showed near perfect classification accuracy on the high SNR dataset, ultimately outperforming both the VGG architecture and baseline approach. empirical investigation of catastrophic forgetting in gradient-based neural I/Q data is a translation of amplitude and phase data from a polar coordinate system to a cartesian coordinate system. The WABBLES network uses multiresolution analysis to look for subtle, yet important features from the input data for a better . 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. It turns out that state of the art deep learning methods can be applied to the same problem of signal classification and shows excellent results while completely avoiding the need for difficult handcrafted feature selection. networks, in, J.Kirkpatrick, R.Pascanu, N.Rabinowitz, J.Veness, G.Desjardins, A. wireless signal spoofing, in. This task aims to explore the strengths and weaknesses of existing data sets and prepare a validated training set to be used in Phase II. We use a weight parameter w[0,1] to combine these two confidences as wcTt+(1w)(1cDt). 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. networks,, W.Lee, M.Kim, D.Cho, and R.Schober, Deep sensing: Cooperative spectrum Component Analysis (ICA) to separate interfering signals. Classification for Real RF Signals, Real-Time and Embedded Deep Learning on FPGA for RF Signal Each of these signals has its ej rotation. We are unfortunately not able to support these and we do not recommend their usage with OmniSIG. classification using deep learning model,, T.OShea, T.Roy, and T.C. Clancy, Over-the-air deep learning based radio The GUI operates in the time-frequency (TF) domain, which is achieved by . 1). The model is trained with an Nvidia Tesla V100 GPU for 16 hours before it finally reaches a stopping point. Herein we explored several ML strategies for RF fingerprinting as applied to the classification and identification of RF Orthogonal Frequency-Division Multiplexing (OFDM) packets ofdm17 : Support Vector Machines (SVM), with two different kernels, Deep Neural Nets (DNN), Convolutional Neural Nets (CNN), and Embedding of 24 modulations using one of our models. After learning the traffic profile of out-network users, signal classification results based on deep learning are updated as follows. This protocol is distributed and only requires in-network users to exchange information with their neighbors. Learn more. 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. Suppose the jammer receives the in-network user signal, which is QAM64 at 18 dB SNR, and collects 1000 samples. Then we apply two different outlier detection approaches to these features. Out-network user success rate is 47.57%. The matrix can also reveal patterns in misidentification. designed a machine learning RF-based DDI system with three machine learning models developed by the XGBoost algorithm, and experimentally verified that the low-frequency spectrum of the captured RF signal in the communication between the UAV and its flight controller as the input feature vector already contains enough . Blindly decoding a signal requires estimating its unknown transmit k-means method can successfully classify all inliers and most of outliers, achieving 0.88 average accuracy. to the outputs of convolutional layers using Minimum Covariance Determinant These modules are not maintained), Larger Version (including AM-SSB): RML2016.10b.tar.bz2, Example ClassifierJupyter Notebook: RML2016.10a_VTCNN2_example.ipynb. be unknown for which there is no training data; 3) signals may be spoofed such We extend the CNN structure to capture phase shift due to radio hardware effects to identify the spoofing signals and relabel them as jammers. We present an. where A denotes the weights used to classify the first five modulations (Task A), LB() is the loss function for Task B, Fi is the fisher information matrix that determines the importance of old and new tasks, and i denotes the parameters of a neural network. One separate time slot is assigned for each in-network user to transmit its data. Here on Medium, we discuss the applications of this tech through our blogs. our results with our data (morad_scatch.ipynb), a notebook that builds a similar model but simplified to classify handwritten digits on the mnist dataset that achieves 99.43% accuracy (mnist_example.ipynb), the notebook we used to get the t-SNE embeddings on training and unlabelled test data to evaluate models (tsne_clean.ipynb), simplified code that can be used to get your own t-SNE embeddings on your own Keras models and plot them interactively using Bokeh if you desire (tsne_utils.py), a notebook that uses tsne_utils.py and one of our models to get embeddings for signal modulation data on training data only (tsne_train_only.ipynb), a notebook to do t-SNE on the mnist data and model (mnist_tsne.ipynb). and download the appropriate forms and rules. 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. That is, if there is no out-network user transmission, it is in state, Initialize the number of state changes as. We split the data into 80% for training and 20% for testing. Next, we consider a smart jammer that records an in-network user signal, and then amplifies and forwards it as a replay attack (instead of transmitting a distinct jamming signal, as assumed before). AQR: Machine Learning Related Research Papers Recommendation, fast.ai Tabular DataClassification with Entity Embedding, Walk through TIMEPart-2 (Modelling of Time Series Analysis in Python). In SectionIII, the test signals are taken one by one from a given SNR. Deep learning (DL) models are the most widely researched AI-based models because of their effectiveness and high performance. For this reason, you should use the agency link listed below which will take you KNN proved to be the second-best classifier, with 97.96% accurate EEG signal classification. jQuery('.alert-message') MCD fits an elliptic envelope to the test data such that any data point outside the ellipse is considered as an outlier. @tYL6-HG)r:3rwvBouYZ?&U"[ fM2DX2lMT?ObeLD0F!`@ Re-training the model using all eight modulations brings several issues regarding memory, computation, and security as follows. We are particularly interested in the following two cases that we later use in the design of the DSA protocol: Superposition of in-network user and jamming signals. Notice that the VGG and ResNet deep learning approaches show vast improvements in classification accuracy for lower value SNR signals when compared to the baseline model. https://www.acq.osd.mil/osbp/sbir/solicitations/index.shtml. Modulation schemes are methods of encoding information onto a high frequency carrier wave, that are more practical for transmission. August 30, 2016, KEYWORDS:Machine Learning, Signatures Modulation Detection And Classification, Amy Modernization Priorities, Modular Open System Architecture, Software/Hardware Convergence, jQuery(document).ready(function($){ classification results in a distributed scheduling protocol, where in-network The only difference is that the last fully connected layer has 17 output neurons for 17 cases corresponding to different rotation angles (instead of 4 output neurons). (MCD) and k-means clustering methods. Machine learning and deep learning technologies are promising an end-to-end optimization of wireless networks while they commoditize PHY and signal-processing designs and help overcome RF complexities This assumption is reasonable for in-network and out-network user signals. modulation type, and bandwidth. The VGG and ResNet performances with respect to accuracy are virtually identical until SNR values exceed 10dB, at which point ResNet is the clear winner. The benchmark performances are given as follows. The first three periods take a fixed and small portion of the superframe. Security: If a device or server is compromised, adversary will have the data to train its own classifier, since previous and new data are all stored. Fig. Integration of the system into commercial autonomous vehicles. The data is divided into 80% for training and 20% for testing purposes. The classifier computes a score vector, We use the dataset in [1]. We introduce the Sig53 dataset consisting of 5 million synthetically-generated samples from 53 different signal classes and expertly chosen impairments. those with radiation Dose > 0 versus 0). This method divides the samples into k=2 clusters by iteratively finding k cluster centers. .css('align-items', 'center') A tag already exists with the provided branch name. 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. The data has been created synthetically by first modulating speech, music and text using standard software. .css('font-weight', '600'); With the widespread adoption of the Internet of Things (IoT), the number of wirelessly connected devices will continue to proliferate over the next few years. SectionII discusses related work. 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 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. The performance of distributed scheduling with different classifiers is shown in TableIV, where random classifier randomly classifies the channel with probability 25%. Improved CNN model for RadioML dataset The signal is separated as two signals and then these separated signals are fed into the CNN classifier for classification into in-network user signals, jamming signals, or out-network user signals. Out-network user success rate is 47.57%. 1300 17th Street North, Suite 1260 Arlington, VA, 22209, Over-the-air deep learning based radio signal classification, (Warning! Your email address will not be published. An example of a skip connection is shown below: The skip-connection effectively acts as a conduit for earlier features to operate at multiple scales and depths throughout the neural network, circumventing the vanishing gradient problem and allowing for the training of much deeper networks than previously possible. In the feature extraction step, we freeze the model in the classifier and reuse the convolutional layers. The average accuracy over all signal-to-noise-ratios (SNRs) is 0.934. Abstract: In this paper, a novel unsupervised machine learning (ML) algorithm is presented for the expeditious radio frequency (RF) fingerprinting of LoRa modulated chirps. 10-(b) for validation accuracy). a machine learning-based RF jamming classification in wireless ad hoc networks is proposed. For the outlier detection, as the waveform dimensions are large, we reuse the convolutional layers of the classifier to extract the features of the received signal. throughput and out-network user success ratio. some signal types are not known a priori and therefore there is no training data available for those signals; signals are potentially spoofed, e.g., a smart jammer may replay received signals from other users thereby hiding its identity; and. In addition to fixed and known modulations for each signal type, we also addressed the practical cases where 1) modulations change over time; 2) some modulations are unknown for which there is no training data; 3) signals are spoofed by smart jammers replaying other signal types; and 4) signals are superimposed with other interfering signals. 1) if transmitted at the same time (on the same frequency). The performance of distributed scheduling with different classifiers is shown in TableV. We compare results with and without consideration of traffic profile, and benchmarks. Embedding showing the legend and the predicted probability for each point. These modules are not maintained), Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License. The signal classification results are used in the DSA protocol that we design as a distributed scheduling protocol, where an in-network user transmits if the received signal is classified as idle or in-network (possibly superimposed). amplitude-phase modulated signals in flat-fading channels,, M.Alsheikh, S.Lin, D.Niyato, and H.Tan, Machine learning in wireless In this project our objective are as follows: 1) Develop RF fingerprinting datasets. [Online]. Machine learning (ML) is an essential and widely deployed technology for controlling smart devices and systems -- from voice-activated consumer devices (cell phones, appliances, digital assistants . perspective of adversarial deep learning, in, C.deVrieze, L.Simic, and P.Mahonen, The importance of being earnest:

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