machine learning for rf signal classificationmary calderon quintanilla

% The status may be idle, in-network, jammer, or out-network. A. Dobre, A.Abdi, Y.Bar-Ness, and W.Su, Survey of automatic modulation 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 . We use the scheduling protocol outlined in Algorithm1 to schedule time for transmission of packets including sensing, control, and user data. 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 We split the data into 80% for training and 20% for testing. This dataset was used for the "Convolutional Radio Modulation Recognition Networks"and "Unsupervised Representation Learning of Structured Radio Communications Signals"papers, found on our Publications Page. 110 0 obj On the other hand adding more layers to a neural network increases the total number of weights and biases, ultimately increasing the complexity of the model. The jammer uses these signals for jamming. For case 4, we apply blind source separation using Independent For this reason, you should use the agency link listed below which will take you BOTH | We propose a machine learning-based solution for noise classification and decomposition in RF transceivers. .css('margin', '0 15px') Update these numbers based on past state i and current predicted state j, i.e., nij=nij+1. .css('font-size', '16px'); In addition, we trained a separate RF model in classification mode to distinguish between exposed and unexposed samples (i.e. The dataset contains several variants of common RF signal types used in satellite communication. US ground force tactical Signals Intelligence (SIGINT) and EW sensors require the ability to rapidly scan large swaths of the RF spectrum and automatically characterize emissions by frequency and. The performance of distributed scheduling with different classifiers is shown in TableIV, where random classifier randomly classifies the channel with probability 25%. generative adversarial networks on digital signal modulation DESCRIPTION:The US Army Communication-Electronics Research Development & Engineering Center (CERDEC) is interested in experimenting with signals analysis tools which can assist Army operators with detecting and identifying radio frequency emissions. Also, you can reach me at moradshefa@berkeley.edu. 2) Develop open set classification approaches which can distinguish between authorized transmitters and malicious transmitters. All datasets provided by Deepsig Inc. are licensed under the Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License (CC BY-NC-SA 4.0). We are trying to build different machine learning models to solve the Signal Modulation Classification problem. The second approach of feature extraction followed by outlier detection yields the best performance. Out-network user success rate is 47.57%. 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. This approach achieves 0.837 average accuracy. The dataset enables experiments on signal and modulation classification using modern machine learning such as deep learning with neural networks. .css('width', '100%') 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. .admin-menu.alert-message { padding-top:25px !important;} 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. The output of convolutional layers in the frozen model are then input to the MCD algorithm. We are trying to build different machine learning models to solve the Signal Modulation Classification problem. Suppose the jammer receives the in-network user signal, which is QAM64 at 18 dB SNR, and collects 1000 samples. 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. We extend the CNN structure to capture phase shift due to radio hardware effects to identify the spoofing signals and relabel them as jammers. modulation type, and bandwidth. Here on Medium, we discuss the applications of this tech through our blogs. 1). Along with this increase, device authentication will become more challenging than ever specially for devices under stringent computation and power budgets. The performance measures are in-network user throughput (packet/slot) and out-network user success ratio (%). The classification of idle, in-network, and jammer corresponds to state 0 in this study. If this combined confidence is smaller than 0.5, we claim that the current state is 1, otherwise the current state is 0. However, we will provide: Simple embedding of our small mnist model (no legend, no prediction probability). Benchmark scheme 1: In-network user throughput is 829. %PDF-1.5 Performance of modulation classification for real RF signals, in, Y.Shi, K.Davaslioglu, and Y.E. Sagduyu, Generative adversarial network for We have the following three cases. Your email address will not be published. We consider different modulation schemes used by different types of users transmitting on a single channel. A deep convolutional neural network architecture is used for signal modulation classification. 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). Scheduling decisions are made using deep learning classification results. signal sources. M.Ring, Continual learning in reinforcement environments, Ph.D. Convolutional Neural Network (CNN) using an Elastic Weight Consolidation (EWC) Higher values on the Fisher diagonal elements Fi indicate more certain knowledge, and thus they are less flexible. A superframe has 10 time slots for data transmission. Then the jammer amplifies and forwards it for jamming. 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. A synthetic dataset, generated with GNU Radio,consisting of 11 modulations. Integration of the system into commercial autonomous vehicles. 1) if transmitted at the same time (on the same frequency). This scheme needs 100 time slots since there are 100 in-network users. The outcome of the deep learning based signal classifier is used by the DSA protocol of in-network users. To this end, we propose an efficient and easy-to-use graphical user interface (GUI) for researchers to collect their own data to build a customized RF classification system. model, in, A.Ali and Y. Y.Tu, Y.Lin, J.Wang, and J.U. Kim, Semi-supervised learning with For case 3, we extend the CNN structure Signal Modulation Classification Using Machine Learning Morad Shefa, Gerry Zhang, Steve Croft. 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 Re-training the model using all eight modulations brings several issues regarding memory, computation, and security as follows. to use Codespaces. We consider a wireless signal classifier that classifies signals based on modulation types into idle, in-network users (such as secondary users), out-network users (such as primary users), and jammers. If the in-network user classifies the received signals as out-network, it does not access the channel. The individual should be capable of playing a key role in a variety of machine learning and algorithm development for next-generation applications; in radar, communications, and electronic warfare. Then based on traffic profile, the confidence of sTt=0 is cTt while based on deep learning, the confidence of sDt=1 is 1cDt. Component Analysis (ICA) to separate interfering signals. Contamination accounts for the estimated proportion of outliers in the dataset. Out-network user success is 16%. 8 shows confusion matrices at 0dB, 10dB, and 18dB SNR levels. stream their actual bandwidths) are centered at 0 Hz (+- random frequency offset, see below) random frequency offset: +- 250 Hz. 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). signal (modulation) classification solution in a realistic wireless network Benchmark scheme 2: In-network throughput is 4196. As instrumentation expands beyond frequencies allocated to radio astronomy and human generated technology fills more of the wireless spectrum classifying RFI as such becomes more important. .css('background', '#FBD04A') to capture phase shifts due to radio hardware effects to identify the spoofing 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. recognition networks, in, B.Kim, J.K. amd H. Chaeabd D.Yoon, and J.W. Choi, Deep neural 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. Therefore, we organized a Special Issue on remote sensing . The architecture contains many convolutional layers (embedded in the residual stack module). 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. Benchmark scheme 2: In-network user throughput is 4145. This approach helps identify and protect weights. A confusion matrix shows how well a model predicts the right label (class) for any query presented to it. We combine these two confidences as w(1cTt)+(1w)cDt. Now, we simulate a wireless network, where the SNR changes depending on channel gain, signals may be received as superposed, signal types may change over time, remain unknown, or may be spoofed by smart jammers. In this study, radio frequency (RF) based detection and classification of drones is investigated. As the name indicates, it is comprised of a number of decision trees. EWC slows down learning on selected neural network weights to remember previously learned tasks (modulations) [28]. Signal Generation Software: https://github.com/radioML/dataset Warning! CERCEC seeks algorithms and implementations of ML to detect and classify Radio Frequency (RF) signals. Neural networks learn by minimizing some penalty function and iteratively updating a series of weights and biases. The best contamination factor is 0.15, which maximizes the minimum accuracy for inliers and outliers. 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. In the feature extraction step, we freeze the model in the classifier and reuse the convolutional layers. In each epoch the network predicts the labels in a feed forward manner. For example, radio-frequency interference (RFI) is a major problem in radio astronomy. 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. RF and DT provided comparable performance with the equivalent . Available: M.Abadi, P.Barham, J.C. abnd Z.Chen, A.Davis, J. .css('text-align', 'center') These modules are not maintained), Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License. Remote sensing is used in an increasingly wide range of applications. The benchmark performances are given as follows. Using 1000 samples for each of 17 rotation angles, we have 17K samples. Then we apply two different outlier detection approaches to these features. SectionII discusses related work. We also . Each signal example in the dataset comes in I/Q data format, a way of storing signal information in such a way that preserves both the amplitude and phase of the signal. AbstractIn recent years, Deep Learning (DL) has been successfully applied to detect and classify Radio Frequency (RF) Signals. . estimation and signal detection in ofdm systems,, Y.Shi, T.Erpek, Y.E. Sagduyu, and J.Li, Spectrum data poisoning with k-means method can successfully classify all inliers and most of outliers, achieving 0.88 average accuracy. The jammer rotates 1000 samples with different angles =k16 for k=0,1,,16. The implementation will also output signal descriptors which may assist a human in signal classification e.g. For case 1, we apply continual learning and train a The data has been created synthetically by first modulating speech, music and text using standard software. Signal classification is an important functionality for cognitive radio applications to improve situational awareness (such as identifying interference sources) and support DSA. 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). The official link for this solicitation is: random phase offset. Traditional machine learning classification methods include partial least squares-discriminant analysis (PLS-DA) , decision trees (DTs) , random forest (RF) , Naive Bayes , the k-nearest neighbor algorithm (KNN) , and support vector machines (SVMs) . 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. In their experiment, Oshea et al. Please A locked padlock) or https:// means you've safely connected to the .gov website. Some signal types such as modulations used in jammer signals are unknown (see case 2 in Fig. We designed and implemented a deep learning based RF signal classifier on the Field Programmable Gate Array (FPGA) of an embedded software-defined radio platform, DeepRadio, that classifies the signals received through the RF front end to different modulation types in real time and with low power. The first three periods take a fixed and small portion of the superframe. Out-network users are treated as primary users and their communications should be protected. signals are superimposed due to the interference effects from concurrent transmissions of different signal types. << /Filter /FlateDecode /Length 4380 >> network-based automatic modulation classification technique, in, G.J. Mendis, J.Wei, and A.Madanayake, Deep learning-based automated Classification algorithms are an important branch of machine learning. There is no expert feature extraction or pre-processing performed on the raw data. Required fields are marked *. 2018: Disease Detection: EMG Signal Classification for Detecting . In-network users that classify received signals to better signal types gain access to channel. 1, ) such that there is no available training data for supervised learning. They also add complexity to a receiver since the raw I/Q data must be manipulated before classification. We split the data into 80% for training and 20% for testing. The assignment of time slots changes from frame to frame, based on traffic and channel status. Instead, the network learns important features on the raw time series data. Superposition of jamming and out-network user signals. Feroz, N., Ahad, M.A., Doja, F. Machine learning techniques for improved breast cancer detection and prognosisA comparative analysis. modulation type, and bandwidth. 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. By adding more layers, you increase the ability of a network to learn hierarchical representations which is often required for many problems in machine learning. There are different reasons why signal modulation classification can be important. Then based on pij, we can classify the current status as sTt with confidence cTt. It turns out you can use state of the art machine learning for this type of classification. train a 121 layer deep ResNet with 220,000 trainable parameters on a dataset of two-million signals. Deliver a prototype system to CERDEC for further testing. These datasets will be made available to the research community and can be used in many use cases. with out-network (primary) users and jammers. we used ns-3 to simulate different jamming techniques on wireless . 11.Using image data, predict the gender and age range of an individual in Python. Now lets switch gears and talk about the neural network that the paper uses. 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. .css('padding', '15px 5px') As the error is received by each layer, that layer figures out how to mathematically adjust its weights and biases in order to perform better on future data. those with radiation Dose > 0 versus 0). After learning the traffic profile of out-network users, signal classification results based on deep learning are updated as follows. that may all coexist in a wireless network. .css('font-size', '12px'); This represents a cleaner and more normalized version of the 2016.04C dataset, which this supersedes. This approach achieves 0.972 accuracy in classifying superimposed signals. RF-Signal-Model. OBJECTIVE:Develop and demonstrate a signatures detection and classification system for Army tactical vehicles, to reduce cognitive burden on Army signals analysts. Out-network user success rate is 47.57%. 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. SectionV concludes the paper. In case 4, we applied ICA to separate interfering signals and classified them separately by deep learning. We first consider the basic setting that there are no outliers (unknown signal types) and no superimposed signals, and traffic profile is not considered. Dataset Download: 2018.01.OSC.0001_1024x2M.h5.tar.gz. Supported by recent computational and algorithmic advances, is promising to extract and operate on latent representations of spectrum data that conventional machine learning algorithms have failed to achieve. Satellite. 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). 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! The "type" or transmission mode of a signal is often related to some wireless standard, for which the waveform has been generated. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. We consider the following simulation setting. This dataset was first released at the 6th Annual GNU Radio Conference. If out-network signals are detected, the in-network user should not transmit to avoid any interference, i.e., out-network users are treated as primary users. These soil investigations are essential for each individual construction site and have to be performed prior to the design of a project. The impact of the number of transmitters used in training on generalization to new transmitters is to be considered. Improved CNN model for RadioML dataset In particular, deep learning can effectively classify signals based on their modulation types. large-scale machine learning, in, D.Kingma and J.Ba, Adam: A method for stochastic optimization,, I.J. Goodfellow, M.Mirza, D.Xiao, A.Courville, and Y.Bengio, An 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. You signed in with another tab or window. 1: RF signal classification cases, including new signals, unknown signals, replay attacks from jammers, and superimposed signals. We now consider the case that initially five modulations are taught to the classifier. var warning_html = '