machine learning for rf signal classification

They report seeing diminishing returns after about six residual stacks. Along with this increase, device authentication will become more challenging than ever specially for devices under stringent computation and power budgets. In the above image you can see how drastically noise can affect our ability to recognize a signal. This approach achieves over time the level of performance similar to the ideal case when there are no new modulations. In case 1, we applied continual learning to mitigate catastrophic forgetting. .css('padding-top', '2px') In case 2, we applied outlier detection to the outputs of convolutional layers by using MCD and k-means clustering methods. .css('text-align', 'center') The file is formatted as a "pickle" file which can be opened for example in Python by using cPickle.load(). Convolutional Neural Network (CNN) using an Elastic Weight Consolidation (EWC) 1) if transmitted at the same time (on the same frequency). In their experiment, Oshea et al. .css('text-decoration', 'underline') 11.Using image data, predict the gender and age range of an individual in Python. We HIGHLY recommend researchers develop their own datasets using basic modulation tools such as in MATLAB or GNU Radio, or use REAL data recorded from over the air! .css('background', '#FBD04A') . 2 out-network users and 2 jammers are randomly distributed in the same region. jQuery('.alert-icon') These modules are not maintained), Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License. Fig. For example, radio-frequency interference (RFI) is a major problem in radio astronomy. 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. These modulations are categorized into signal types as discussed before. 1) and should be classified as specified signal types. The model also performs reasonably well across most signal types as shown in the following confusion matrix. Classification for Real RF Signals, Real-Time and Embedded Deep Learning on FPGA for RF Signal The point over which we hover is labelled 1 with predicted probability 0.822. If you want to skip all the readings and want to see what we provide and how you can use our code feel free to skip to the final section. jQuery('.alert-link') adversarial deep learning, in, Y.Shi, Y.E. Sagduyu, T.Erpek, K.Davaslioglu, Z.Lu, and J.Li, For comparison, the authors also ran the same experiment using a VGG convolutional neural network and a boosted gradient tree classifier as a baseline. However, while recognized datasets exist in certain domains such as speech, handwriting and object recognition, there are no equivalent robust and comprehensive datasets in the wireless communications and radio frequency (RF) signals domain. signal separation, in, O. 2018: Disease Detection: EMG Signal Classification for Detecting . 13) that consists of four periods: Spectrum sensing collects I&Q data on a channel over a sensing period. We have the following three cases. network-based automatic modulation classification technique, in, G.J. Mendis, J.Wei, and A.Madanayake, Deep learning-based automated Benchmark scheme 1: In-network throughput is 760. This is called the vanishing gradient problem which gets worse as we add more layers to a neural network. Wireless transmitters are affected by various noise sources, each of which has a distinct impact on the signal constellation points. These include use of radar sensors, electro-optical cameras, thermal cameras and acoustic sensors. 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. Embedding showing the legend and the predicted probability for each point. The GUI operates in the time-frequency (TF) domain, which is achieved by . modulation type, and bandwidth. 6, we can see that EWC mitigates catastrophic learning to improve the accuracy on Task B such that the accuracy increases over time to the level of Task A. 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. In addition, we trained a separate RF model in classification mode to distinguish between exposed and unexposed samples (i.e. A clean signal will have a high SNR and a noisy signal will have a low SNR. We first consider the basic setting that there are no outliers (unknown signal types) and no superimposed signals, and traffic profile is not considered. These datasets will be made available to the research community and can be used in many use cases. 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 "type" or transmission mode of a signal is often related to some wireless standard, for which the waveform has been generated. Training happens over several epochs on the training data. classification,, This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. based loss. We apply blind source separation using Independent Component Analysis (ICA) [9] to obtain each single signal that is further classified by deep learning. The deep learning method relies on stochastic gradient descent to optimize large parametric neural network models. xZ[s~#U%^'rR[@Q z l3Kg~{C_dl./[$^vqW\/n.c/2K=`7tZ;(U]J;F{ u~_: g#kYlF6u$pzB]k:6y_5e6/xa5fuq),|1gj:E^2~0E=? Zx*t :a%? However, when the filter size in the convolutional layers is not divisible by the strides, it can create checkerboard effects (see, Convolutional layer with 128 filters with size of (3,3), 2D MaxPolling layer with size (2,1) and stride (2,1), Convolutional layer with 256 filters with size of (3,3), 2D MaxPolling layer with pool size (2,2) and stride (2,1), Fully connected layer with 256neurons and Scaled Exponential Linear Unit (SELU) activation function, which is x if x>0 and aexa if x0 for some constant a, Fully connected layer with 64 neurons and SELU activation function, Fully connected layer with 4 neurons and SELU activation function, and the categorical cross-entropy loss function is used for training. Introduction. One issue you quickly run into as you add more layers is called the vanishing gradient problem, but to understand this we first need to understand how neural networks are trained. The dataset enables experiments on signal and modulation classification using modern machine learning such as deep learning with neural networks. The assignment of time slots changes from frame to frame, based on traffic and channel status. Rusu, K.Milan, J.Quan, T.Ramalho, T.Grabska-Barwinska, and D.Hassabis, Signal classification is an important functionality for cognitive radio applications to improve situational awareness (such as identifying interference sources) and support DSA. to the outputs of convolutional layers using Minimum Covariance Determinant Adversarial deep learning for cognitive radio security: Jamming attack and to capture phase shifts due to radio hardware effects to identify the spoofing If this combined confidence is smaller than 0.5, we claim that the current state is 1, otherwise the current state is 0. sTt=1 and sDt=0. Automated Cataract detection in Images using Open CV and Python Part 1, The brilliance of Generative Adversarial Networks(GANs) in DALL-E, Methods you need know to Estimate Feature Importance for ML models. Wireless Signal Recognition with Deep Learning. We use patience of 8 epochs (i.e., if loss at epoch t did not improve for 8 epochs, we stop and take the best (t8) result) and train for 200 iterations. Out-network user success is 16%. We start with the simple baseline scenario that all signal types (i.e., modulations) are fixed and known (such that training data are available) and there are no superimposed signals (i.e., signals are already separated). 1:Army Modernization Priorities Directive 2017-33, 2: Vincent Boulanin and Maaike Vebruggen: November 30, 2017: "Mapping the Development of Autonomy on Weapon Systems" https://www.sipri.org//siprireport_mapping_the_development_of_autonomy_in_weap, 3: A. Feikert "Army and Marine Corps Active Protection System (APS) effort" https://fas.org/sgp/crs/weapons/R44598.pdf. Out-network user success is 47.57%. This represents a cleaner and more normalized version of the 2016.04C dataset, which this supersedes. Signal Modulation Classification Using Machine Learning Morad Shefa, Gerry Zhang, Steve Croft. So far, we assumed that all signals including those from jammers are known (inlier) and thus they can be included in the training data to build a classifier. 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. A DL approach is especially useful since it identies the presence of a signal without needing full protocol information, and can also detect and/or classify non-communication wave-forms, such as radar signals. In Fig. @tYL6-HG)r:3rwvBouYZ?&U"[ fM2DX2lMT?ObeLD0F!`@ We present next how to learn the traffic profile of out-network users and use it for signal classification. In all the cases considered, the integration of deep learning based classifier with distributed scheduling performs always much better than benchmarks. 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). This scheme needs 100 time slots since there are 100 in-network users. For case 1, we apply continual learning and train a MCD fits an elliptic envelope to the test data such that any data point outside the ellipse is considered as an outlier. 7. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. .css('font-size', '16px'); Dean, M.Devin, DeepSig provides several supported and vetted datasets for commercial customers which are not provided here -- unfortunately we are not able to provide support, revisions or assistance for these open datasets due to overwhelming demand! Deliver a prototype system to CERDEC for further testing. 1.1. The goal is to improve both measures. Contamination accounts for the estimated proportion of outliers in the dataset. The jammer rotates 1000 samples with different angles =k16 for k=0,1,,16. (MCD) and k-means clustering methods. The evaluation settings are as the following: Inlier signals: QPSK, 8PSK, CPFSK, AM-SSB, AM-DSB, GFSK, Outlier signals: QAM16, QAM64, PAM4, WBFM. Results demonstrate the feasibility of using deep learning to classify RF signals with high accuracy in unknown and dynamic spectrum environments. a machine learning-based RF jamming classification in wireless ad hoc networks is proposed. Now lets switch gears and talk about the neural network that the paper uses. Computation: Retraining using the complete dataset will take longer. with out-network (primary) users and jammers. The boosted gradient tree is a different kind of machine learning technique that does not learn on raw data and requires hand crafted feature extractors. Neural networks learn by minimizing some penalty function and iteratively updating a series of weights and biases. Performance of modulation classification for real RF signals, in, Y.Shi, K.Davaslioglu, and Y.E. Sagduyu, Generative adversarial network for covariance determinant estimator,, Virginia Polytechnic Institute and State University, DeepWiFi: Cognitive WiFi with Deep Learning, The Importance of Being Earnest: Performance of Modulation 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($){ However, these two approaches require expert design or knowledge of the signal. This method divides the samples into k=2 clusters by iteratively finding k cluster centers. 110 0 obj We are trying to build different machine learning models to solve the Signal Modulation Classification problem. random phase offset. In the feature extraction step, we freeze the model in the classifier and reuse the convolutional layers. Are you sure you want to create this branch? As the loss progresses backwards through the network, it can become smaller and smaller, slowing the learning process. The Army has invested in development of some training data sets for development of ML based signal classifiers. Examples of how information can be transmitted by changing the shape of a carrier wave. As we can see the data maps decently into 10 different clusters. decisions and share the spectrum with each other while avoiding interference Modulation schemes are methods of encoding information onto a high frequency carrier wave, that are more practical for transmission. RF communication systems use advanced forms of modulation to increase the amount of data that can be transmitted in a given amount of frequency spectrum. 1I}3'3ON }@w+ Q8iA}#RffQTaqSH&8R,fSS$%TOp(e affswO_d_kgWVv{EmUl|mhsB"[pBSFWyDrC 2)t= t0G?w+omv A+W055fw[ The model is trained with an Nvidia Tesla V100 GPU for 16 hours before it finally reaches a stopping point. 7 So innovative combination of SVD imaging markers and clinical predictors using different ML algorithms such as random forest (RF) and eXtreme Gradient Boosting . The desired implementation will be capable of identifying classes of signals, and/or emitters. The jammer uses these signals for jamming. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 3, as a function of training epochs. SectionIV introduces the distributed scheduling protocol as an application of deep learning based spectrum analysis. A dataset which includes both synthetic simulated channel effects of 24 digital and analog modulation types which has been validated. After learning the traffic profile of out-network users, signal classification results based on deep learning are updated as follows. 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 consider the following simulation setting. artifacts, 2016. [Online]. Acquire, and modify as required, a COTS hardware and software. The performance with and without traffic profile incorporated in signal classification is shown in TableVI.

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