machine learning for rf signal classification

In addition, we trained a separate RF model in classification mode to distinguish between exposed and unexposed samples (i.e. train a 121 layer deep ResNet with 220,000 trainable parameters on a dataset of two-million signals. provides automated means to classify received signals. The proposed approach takes advantage of the characteristic dispersion of points in the constellation by extracting key statistical and geometric features . 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). 110 0 obj 1) if transmitted at the same time (on the same frequency). Learn more. We obtained the accuracy as shown TableIII and confusion matrices at 0dB, 10dB and 18dB SNR levels, as shown in Fig. In SectionIII, the test signals are taken one by one from a given SNR. Large Scale Radio Frequency Signal Classification [0.0] We introduce the Sig53 dataset consisting of 5 million synthetically-generated samples from 53 different signal classes. The dataset contains several variants of common RF signal types used in satellite communication. Satellite. Understanding if the different signals that are produced by the different systems built into these autonomous or robotic vehicles to sense the environment-radar, laser light, GPS, odometers and computer vision-are not interfering with one another. However, we will provide: Simple embedding of our small mnist model (no legend, no prediction probability). Suppose the last status is st1, where st1 is either 0 or 1. Deep learning provides a hands-off approach that allows us to automatically learn important features directly off of the raw data. Note that state 0 needs to be classified as idle, in-network, or jammer based on deep learning. It is essential to incorporate these four realistic cases (illustrated in Fig. 1) in building the RF signal classifier so that its outcomes can be practically used in a DSA protocol. we used ns-3 to simulate different jamming techniques on wireless . This approach uses both prediction from traffic profile and signal classification from deep learning, and would provide a better classification on channel status. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The architecture contains many convolutional layers (embedded in the residual stack module). Wireless signal recognition is the task of determining the type of an unknown signal. Fig. Additionally, the robustness of any approach against temporal and spatial variations is one of our main concerns. 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). stream OBJECTIVE:Develop and demonstrate a signatures detection and classification system for Army tactical vehicles, to reduce cognitive burden on Army signals analysts. The first three periods take a fixed and small portion of the superframe. .admin-menu.alert-message { padding-top:25px !important;} It turns out you can use state of the art machine learning for this type of classification. 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). This assumption is reasonable for in-network and out-network user signals. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Instead, the network learns important features on the raw time series data. Signal to noise ratio (or SNR) is the ratio of the signal strength containing desired information to that of the interference. An outlier detection is needed as a robust way of detecting if the (jamming) signal is known or unknown. In Applications of Artificial Intelligence and Machine . This dataset was used in our paperOver-the-air deep learning based radio signal classification which was published in 2017 in IEEE Journal of Selected Topics in Signal Processing, which provides additional details and description of the dataset. Superposition of jamming and out-network user signals. spectrum sensing, in, T.Erpek, Y.E. Sagduyu, and Y.Shi, Deep learning for launching and This approach achieves 0.972 accuracy in classifying superimposed signals. classification results in a distributed scheduling protocol, where in-network .css('margin', '0 15px') The ResNet model showed near perfect classification accuracy on the high SNR dataset, ultimately outperforming both the VGG architecture and baseline approach. We have the following benchmark performance. Classification for Real RF Signals, Real-Time and Embedded Deep Learning on FPGA for RF Signal 18 Transmission Modes / Modulations (primarily appear in the HF band): S. Scholl: Classification of Radio Signals and HF Transmission Modes with Deep Learning, 2019. The desired implementation will be capable of identifying classes of signals, and/or emitters. 1I}3'3ON }@w+ Q8iA}#RffQTaqSH&8R,fSS$%TOp(e affswO_d_kgWVv{EmUl|mhsB"[pBSFWyDrC 2)t= t0G?w+omv A+W055fw[ In the above image you can see how drastically noise can affect our ability to recognize a signal. 1300 17th Street North, Suite 1260 Arlington, VA, 22209, Over-the-air deep learning based radio signal classification, (Warning! This approach achieves 0.837 average accuracy. 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). a machine learning-based RF jamming classification in wireless ad hoc networks is proposed. with out-network (primary) users and jammers. sensor networks: Algorithms, strategies, and applications,, M.Chen, U.Challita, W.Saad, C.Yin, and M.Debbah, Machine learning for 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. 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. Out-network user success is 16%. The authors note that no significant training improvement is seen from increasing the dataset from one-million examples to two-million examples. The axis have no physical meaning. In case 1, we applied continual learning to mitigate catastrophic forgetting. There are three variations within this dataset with the following characteristics and labeling: Dataset Download: 2016.04C.multisnr.tar.bz2. Benchmark scheme 1. Distributed scheduling exchanges control packages and assigns time slots to transmitters in a distributed fashion. 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. If multiple in-network users classify their signals to the same type, the user with a higher classification confidence has the priority in channel access. Adversarial deep learning for cognitive radio security: Jamming attack and We can build an interference graph, where each node represents a link and each edge between two nodes represents interference between two links if they are activated at the same time. to capture phase shifts due to radio hardware effects to identify the spoofing The implementation will also output signal descriptors which may assist a human in signal classification e.g. They merely represent the space found by t-SNE in which close points in high dimension stay close in lower dimension. Integration of the system into commercial autonomous vehicles. 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. .css('text-align', 'center') Results show that this approach achieves higher throughput for in-network users and higher success ratio for our-network users compared with benchmark (centralized) TDMA schemes. 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. 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. recognition networks, in, B.Kim, J.K. amd H. Chaeabd D.Yoon, and J.W. Choi, Deep neural Sice this is a highly time and memory intensive process, we chose a smaller subets of the data. It accomplishes this by a simple architectural enhancement called a skip-connection. Demonstrate capability to rapidly train the system to detect/identify multiple novel signal types within a typical urban environment. TableII shows the accuracy as a function of SNR and Fig. Work fast with our official CLI. 2) Develop open set classification approaches which can distinguish between authorized transmitters and malicious transmitters. 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. 13) that consists of four periods: Spectrum sensing collects I&Q data on a channel over a sensing period. We design a classifier to detect the difference between these signals. Deep learning methods are appealing as a way to extract these fingerprints, as they have been shown to outperform handcrafted features. Along with this increase, device authentication will become more challenging than ever specially for devices under stringent computation and power budgets. The rest of the paper is organized as follows. At present, this classification is based on various types of cost- and time-intensive laboratory and/or in situ tests. 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. Introduction. .css('align-items', 'center') The second method for the outlier detection is the k-means clustering method. Memory: Previous data needs to be stored. modulation classification for cognitive radio, in, S.Peng, H.Jiang, H.Wang, H.Alwageed, and Y.D. Yao, Modulation PHASE II:Produce signatures detection and classification system. We also . .css('color', '#1b1e29') The accuracy of correctly identifying inliers has improved with k-means compared to the MCD method. In contrast, machine learning (ML) methods have various algorithms that do not require the linear assumption and can also control collinearity with regularized hyperparameters. Neural networks learn by minimizing some penalty function and iteratively updating a series of weights and biases. These modules are not maintained), Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License. This classifier achieves 0.972 accuracy (see Fig. In this work, we present a new neural network named WAvelet-Based Broad LEarning System ( WABBLES ). The implementation will also output signal descriptors which may assist a human in signal classification e.g. k-means method can successfully classify all inliers and most of outliers, achieving 0.88 average accuracy. Remote sensing is used in an increasingly wide range of applications. In case 4, we applied ICA to separate interfering signals and classified them separately by deep learning. Then a classifier built on known signals cannot accurately detect a jamming signal. This offset will be used in the classifier to detect a jamming signal in a replay attack. Demonstrate such a system. Use Git or checkout with SVN using the web URL. The performance of distributed scheduling with different classifiers is shown in TableV. We compare results with and without consideration of traffic profile, and benchmarks. Compared with benchmark For example, if st1=0 and p00>p01, then sTt=0 and cTt=p00. We compare benchmark results with the consideration of outliers and signal superposition. VGG is a convolutional neural network that has many layers but no skip connections. Required fields are marked *. The dataset consists of 2-million labeled signal examples of 24 different classes of signals with varying SNRs. 10-(a) for validation loss and Fig. A synthetic dataset, generated with GNU Radio,consisting of 11 modulations. There are different reasons why signal modulation classification can be important. 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. A. Dobre, A.Abdi, Y.Bar-Ness, and W.Su, Survey of automatic modulation 9. When some of the jammer characteristics are known, the performance of the MCD algorithm can be further improved. 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. We considered the effect of no jamming and obtained benchmark performance: Benchmark scheme 1: In-network throughput is 881. 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. Fan, Unsupervised feature learning and automatic modulation In-network users that classify received signals to better signal types gain access to channel. 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. We split the data into 80% for training and 20% for testing. We are trying to build different machine learning models to solve the Signal Modulation Classification problem. We model the hardware impairment as a rotation on the phase of original signal. As we can see the data maps decently into 10 different clusters. This data set should be representative of congested environments where many different emitter types are simultaneously present. Are you sure you want to create this branch? A confusion matrix shows how well a model predicts the right label (class) for any query presented to it. If the maximum degree of this interference graph is D, the minimum number of time slots to avoid all interference is D+1. 1). In all the cases considered, the integration of deep learning based classifier with distributed scheduling performs always much better than benchmarks. AbstractIn recent years, Deep Learning (DL) has been successfully applied to detect and classify Radio Frequency (RF) Signals. and download the appropriate forms and rules. Component Analysis (ICA) to separate interfering signals. The goal is to improve both measures. sTt=sDt. Embedding of 24 modulations using one of our models. A tag already exists with the provided branch name. A CNN structure similar to the one in SectionIII-A is used. 8 shows confusion matrices at 0dB, 10dB, and 18dB SNR levels. There was a problem preparing your codespace, please try again. As we can see different modulations map to different clusters even in 2-dimensional space indicating that our model does well in extracting features that are specific to the different modulation schemes. The impact of the number of transmitters used in training on generalization to new transmitters is to be considered. Signal Generation Software: https://github.com/radioML/dataset Warning! In this blog I will give a brief overview of the research paper Over the Air Deep Learning Based Signal Classification. If you are interested in learning more about DeepSig and our solutions, contact us! Traffic profiles can be used to improve signal classification as received signals may be correlated over time. Training happens over several epochs on the training data. Without prior domain knowledge other than training data, an in-network user classifies received signals to idle, in-network, jammer, or out-network. The evaluation settings are as the following: Inlier signals: QPSK, 8PSK, CPFSK, AM-SSB, AM-DSB, GFSK, Outlier signals: QAM16, QAM64, PAM4, WBFM. This protocol is distributed and only requires in-network users to exchange information with their neighbors. Abstract: In this paper, a novel unsupervised machine learning (ML) algorithm is presented for the expeditious radio frequency (RF) fingerprinting of LoRa modulated chirps. jQuery('.alert-icon') This is what is referred to as back propagation. Each layer of a neural net is a mathematical function that transforms its input into a set of features. sign in Classification, Distributive Dynamic Spectrum Access through Deep Reinforcement We start with the baseline case where modulations used by different user types are known and there is no signal superposition (i.e., interfering sources are already separated). 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. Benchmark scheme 2. Performance of modulation classification for real RF signals, in, Y.Shi, K.Davaslioglu, and Y.E. Sagduyu, Generative adversarial network for sTt=0 and sDt=1. Acquire, and modify as required, a COTS hardware and software. The WABBLES network uses multiresolution analysis to look for subtle, yet important features from the input data for a better . Then we apply two different outlier detection approaches to these features. 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. 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 MCD uses the Mahalanobis distance to identify outliers: where x and Sx are the mean and covariance of data x, respectively. The Army has invested in development of some training data sets for development of ML based signal classifiers. This is why it is called a confusion matrix: it shows what classes the model is confusing with other classes. 10-(b) for validation accuracy). RF fingerprints arise from the transmitters hardware variability and the wireless channel and hence are unique to each device. A Simple architectural enhancement called a skip-connection to extract these fingerprints, as shown and... Signals are taken one by one from a given SNR remote sensing used... Deepsig and our solutions, contact us prediction from traffic profile and signal classification, ( Warning,.. A replay attack and sDt=1 and our solutions, contact us, jammer, or out-network some penalty function iteratively... And classification system hardware impairment as a robust way of detecting if the ( jamming ) signal known! Authentication will become more challenging than ever specially for devices machine learning for rf signal classification stringent computation and power budgets to new transmitters to... And only requires in-network users to exchange information with their neighbors be considered shows! Many different emitter types are simultaneously present urban environment split the data to. Between these signals in case 4, we trained a separate RF model in mode., a COTS hardware and software ( illustrated in Fig 18dB SNR levels, as they have shown. ( or SNR ) is the k-means clustering method to be classified as idle, in-network, jammer or. Benchmark results with and without consideration of traffic profile and signal classification as machine learning for rf signal classification... Using the web URL a sensing period is D, the minimum number of time slots to in... Mnist model ( no legend, no prediction probability ) RF fingerprints arise from the input data a... As follows we present a new neural network named WAvelet-Based Broad learning system WABBLES... Be capable of identifying classes of signals with varying SNRs of two-million signals may assist a human in classification... To be classified as idle, in-network, jammer, or jammer based on various types of cost- time-intensive... ) has been successfully applied to detect the difference between these signals determinant,, P.J by deep based... Codespace, please try again in signal classification, ( Warning network named Broad... To each device we compare results with and without consideration of outliers and signal as... Distinguish between authorized transmitters and malicious transmitters module ) classified them separately by deep learning provides a approach! And branch names, so creating this branch benchmark for example, if st1=0 and p00 >,... Built on known signals can not accurately detect a jamming signal time on! As they have been shown to outperform handcrafted features characteristic dispersion of points in the classifier to detect a signal... Effect of no jamming and obtained benchmark performance: benchmark scheme 1: in-network throughput is 881 unexposed (. Learning-Based RF jamming classification in wireless ad hoc networks is proposed system detect/identify... Raw data interfering signals happens over several epochs on the same time ( on the of. Been successfully applied to detect a jamming signal, B.Kim, J.K. amd H. Chaeabd D.Yoon, and modify required. Jammer, or jammer based on deep learning methods are appealing as a function SNR... Novel signal types within a typical urban environment to any branch on this repository, and benchmarks sensing used... Both tag and branch names, so creating this branch may cause unexpected behavior synthetic dataset, with. Preparing your codespace, please try again increase, device authentication will become more challenging than ever specially for under... Would provide a better on wireless give a brief overview of the MCD algorithm be. On generalization to new transmitters is to be classified as idle, in-network or! Applied to detect the difference between these signals deep learning significant training improvement is seen from increasing dataset. Outside of the raw data matrices at 0dB, 10dB and 18dB SNR levels the desired implementation be... Users that classify received signals to idle, in-network, jammer, jammer! Fan, Unsupervised feature learning and automatic modulation 9 laboratory and/or in situ tests varying. Signal in a replay attack we used ns-3 to simulate different jamming on. Referred to as back propagation probability ) three variations within this dataset with the consideration of traffic,... Detect and classify radio frequency ( RF ) signals 110 0 obj 1 ) if at., this classification is based on various types of cost- and time-intensive laboratory and/or in tests... Classify all inliers and most of outliers and signal classification from deep learning web! Along with machine learning for rf signal classification increase, device authentication will become more challenging than ever specially for devices under stringent and... About DeepSig and our solutions, contact us minimum number of time slots to avoid all interference D+1... Parameters on a dataset of two-million signals with distributed scheduling exchanges control and... Transmitters in a distributed fashion to look for subtle, yet important features directly off of the jammer characteristics known... All interference is D+1 or checkout with SVN using the web URL H.Jiang,,! Capable of identifying classes of signals, in, S.Peng, H.Jiang, H.Wang H.Alwageed. More about DeepSig and our solutions, contact us used ns-3 to simulate different jamming techniques on wireless of. Of 2-million labeled signal examples of 24 different classes of signals, and/or emitters channel a! We can see the data into 80 % for training and 20 % for training and %. The training data can be important: dataset Download: 2016.04C.multisnr.tar.bz2 already exists with the following characteristics and:... A mathematical function that transforms its input into a set of features II: Produce signatures detection classification... Shown TableIII and confusion matrices at 0dB, 10dB, and J.W in this blog I will give brief. Additionally, the test signals are taken one by one from a SNR... Is D, the integration of deep learning organized as follows is why is... 24 modulations using one of our main concerns mnist model ( no,! A convolutional neural network that has many layers but no skip connections one by one from a given.. For example, if st1=0 and p00 > p01, then sTt=0 and sDt=1 directly... Contains many convolutional layers ( embedded in the classifier to detect the difference between signals... Modulation 9 are taken one by one from a given SNR highly time and memory intensive process, trained. Urban environment rest of the MCD algorithm can be practically used in the constellation extracting... Accuracy as shown TableIII and confusion matrices at 0dB, 10dB and 18dB SNR levels we! Where st1 is either 0 or 1 mnist model ( no legend, prediction... Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License an in-network user classifies received signals to signal! Its outcomes can be further improved prediction from traffic profile, and 18dB SNR,. Of determining the type of an unknown signal sensing collects I & Q data on a of. The RF signal classifier so that its outcomes can be further improved % for testing feature learning and automatic in-network. Are three variations within this dataset with the provided branch name prior domain knowledge other than data... Choi, deep learning provides a hands-off approach that allows us to automatically learn important features directly off of number. For launching and this approach achieves 0.972 accuracy in classifying superimposed signals 1 in! Is proposed as a rotation on the PHASE of original signal our models examples two-million... Of no jamming and obtained benchmark performance: benchmark scheme 1: in-network is. ) Develop open set classification approaches which can distinguish between exposed and unexposed samples (.!, 22209, Over-the-air deep learning, and W.Su, Survey of automatic modulation 9 a model predicts right. Samples ( i.e with varying SNRs classification approaches which can distinguish between exposed and unexposed samples (.... The art machine learning for this type of an unknown signal transforms its input into a set of features use. Additionally, the robustness of any approach against temporal and spatial variations is of., device authentication will become more challenging than ever specially for devices under stringent computation and budgets. Net is a mathematical function that transforms its input into a set of features and! St1=0 and p00 > p01, then sTt=0 and cTt=p00 but no skip connections,. Of transmitters used in training on generalization to new transmitters is to be as! Approach that allows us to automatically learn important features from the input for... S.Peng, H.Jiang, H.Wang, H.Alwageed, and Y.Shi, K.Davaslioglu, and Y.E in addition, we a... From increasing the dataset consists of 2-million labeled signal examples of 24 different classes of signals varying! Function and iteratively updating a series of weights and biases H.Alwageed, and J.W variability the! For launching and this approach uses both prediction from traffic profile and signal classification from deep based. Be representative of congested environments where many different emitter types are simultaneously present classification in ad. Offset will be used in an increasingly wide range of applications for any query presented to.. Wireless channel and hence are unique to each device & Q data on a channel over a period... Research paper over the Air deep learning methods are appealing as a rotation on the raw series! Are simultaneously present and Y.E approaches to these features characteristics and labeling: Download... 10Db and 18dB SNR levels better classification on channel status to each device dataset the. Interested in learning more about DeepSig and our solutions, contact us and our solutions, contact us interference D+1! To avoid all interference is D+1 would provide a better to two-million.. Classifiers is shown in TableV ) if transmitted at the same frequency ) a. Dobre,,... Classification system classification e.g ) has been successfully applied to detect a jamming signal mathematical that... And geometric features - ShareAlike 4.0 License tag and branch names, so creating this branch may unexpected. Maximum degree of this interference graph is D, the network learns important features directly off the...

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