Picture credit: Tait Radio Academy, Dimensionality reduction using t-distributed stochastic neighbor embedding (t-SNE) and principal component analysis (PCA) to visualize feature extraction and diagnose problems of the architecture. 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 2 out-network users and 2 jammers are randomly distributed in the same region. jQuery('.alert-content') In Applications of Artificial Intelligence and Machine . jQuery('.alert-icon') A superframe has 10 time slots for data transmission. We generate another instance with p00=p11=0.8 and p01=p10=0.2. sTt=0 and sDt=1. It is essential to incorporate these four realistic cases (illustrated in Fig. Wireless signals are received as superimposed (see case 4 in Fig. Acquire, and modify as required, a COTS hardware and software. 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). . (Warning! << /Filter /FlateDecode /Length 4380 >> 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. You signed in with another tab or window. A locked padlock) or https:// means you've safely connected to the .gov website. Benchmark scheme 1. We studied deep learning based signal classification for wireless networks in presence of out-network users and jammers. Instead of retraining the signal classifier, we design a continual learning algorithm [8] to update the classifier with much lower cost, namely by using an Elastic Weight Consolidation (EWC). We introduce the Sig53 dataset consisting of 5 million synthetically-generated samples from 53 different signal classes and expertly chosen impairments. Suppose the last status is st1, where st1 is either 0 or 1. In Fig. 7. At present, this classification is based on various types of cost- and time-intensive laboratory and/or in situ tests. be unknown for which there is no training data; 3) signals may be spoofed such This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Deep learning provides a hands-off approach that allows us to automatically learn important features directly off of the raw data. 9. From best to worst, other types of received signals are ordered as idle, in-network, and jammer. signal classification,. CERCEC seeks algorithms and implementations of ML to detect and classify Radio Frequency (RF) signals. This approach achieves over time the level of performance similar to the ideal case when there are no new modulations. We have the following three cases. It accomplishes this by a simple architectural enhancement called a skip-connection. By learning from spectrum data, machine learning has found rich applications in wireless communications [13, 14]. RF fingerprints arise from the transmitters hardware variability and the wireless channel and hence are unique to each device. mitigating wireless jamming attacks,, H.Ye, G.Y. Li, and B.H. Juang, Power of deep learning for channel .css('font-size', '16px'); 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. 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. This approach achieves 0.972 accuracy in classifying superimposed signals. Rukshan Pramoditha. Higher values on the Fisher diagonal elements Fi indicate more certain knowledge, and thus they are less flexible. modulation type, and bandwidth. 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. With the dataset from RadioML, we work from 2 approaches to improve the classification performance for the dataset itself and its subset:. A synthetic dataset, generated with GNU Radio, consisting of 11 modulations (8 digital and 3 analog) at varying signal-to-noise ratios. 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%? Deliver a prototype system to CERDEC for further testing. signal (modulation) classification solution in a realistic wireless network .admin-menu.alert-message { padding-top:25px !important;} Introduction. 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). These soil investigations are essential for each individual construction site and have to be performed prior to the design of a project. As the loss progresses backwards through the network, it can become smaller and smaller, slowing the learning process. When some of the jammer characteristics are known, the performance of the MCD algorithm can be further improved. .css('text-decoration', 'underline') State transition probability is calculated as pij=nij/(ni0+ni1). Benchmark scheme 2: In-network throughput is 3619. In contrast, machine learning (ML) methods have various algorithms that do not require the linear assumption and can also control collinearity with regularized hyperparameters. We propose a machine learning-based solution for noise classification and decomposition in RF transceivers. Models and methodologies based on artificial intelligence (AI) are commonly used to increase the performance of remote sensing technologies. 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. Each sample in the dataset consists of 128 complex valued data points, i.e., each data point has the dimensions of (128,2,1) to represent the real and imaginary components. In the feature extraction step, we freeze the model in the classifier and reuse the convolutional layers. The dataset consists of 2-million labeled signal examples of 24 different classes of signals with varying SNRs. These datasets are to include signals from a large number of transmitters under varying signal to noise ratios and over a prolonged period of time. We optimally assign time slots to all nodes to minimize the number of time slots. 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 The GUI operates in the time-frequency (TF) domain, which is achieved by . 1000 superframes are generated. 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). Share sensitive information only on official, secure websites. with out-network (primary) users and jammers. 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. amplitude-phase modulated signals in flat-fading channels,, M.Alsheikh, S.Lin, D.Niyato, and H.Tan, Machine learning in wireless The assignment of time slots changes from frame to frame, based on traffic and channel status. k-means method can successfully classify all inliers and most of outliers, achieving 0.88 average accuracy. After learning the traffic profile of out-network users, signal classification results based on deep learning are updated as follows. A synthetic dataset, generated with GNU Radio,consisting of 11 modulations. Signal Modulation Classification Using Machine Learning Morad Shefa, Gerry Zhang, Steve Croft. 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 . jQuery('.alert-message') 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. In case 2, we applied outlier detection to the outputs of convolutional layers by using MCD and k-means clustering methods. Out-network user success is 16%. sensing based on convolutional neural networks,, K.Davaslioglu and Y.E. Sagduyu, Generative adversarial learning for 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. 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. Data transmission period is divided into time slots and each transmitter sends data in its assigned time slots. Then we apply two different outlier detection approaches to these features. However, an intruder can be any device outside of this set. Wireless transmitters are affected by various noise sources, each of which has a distinct impact on the signal constellation points. 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. CNN models to solve Automatic Modulation Classification problem. 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. Signal Generation Software: https://github.com/radioML/dataset Warning! This is why it is called a confusion matrix: it shows what classes the model is confusing with other classes. 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 our architecture, we use 1D layers and convolutions, but the skip connection is generic for any kind of neural network. https://github.com/radioML/dataset Warning! Your email address will not be published. The performance of distributed scheduling with different classifiers is shown in TableV. We compare results with and without consideration of traffic profile, and benchmarks. 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. 1) in building the RF signal classifier so that its outcomes can be practically used in a DSA protocol. The first three periods take a fixed and small portion of the superframe. The architecture contains many convolutional layers (embedded in the residual stack module). Convolutional Neural Network (CNN) using an Elastic Weight Consolidation (EWC) Here is the ResNet architecture that I reproduced: Notice a few things about the architecture: Skip connections are very simple to implement in Keras (a Python neural network API) and we will talk about this more in my next blog. Neural networks learn by minimizing some penalty function and iteratively updating a series of weights and biases. Classification algorithms are an important branch of machine learning. If the maximum degree of this interference graph is D, the minimum number of time slots to avoid all interference is D+1. A perfect classification would be represented by dark blue along the diagonal and white everywhere else. Out-network user success is 47.57%. 1.1. An innovative and ambitious electrical engineering professional with an interest in<br>communication and signal processing, RF & wireless communication, deep learning, biomedical engineering, IoT . These datasets are from early academic research work in 2016/2017, they have several known errata and are NOT currently used within DeepSig products. Out-network user success rate is 47.57%. Use Git or checkout with SVN using the web URL. We present a deep learning based We split the data into 80% for training and 20% for testing. 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. We combine these two confidences as w(1cTt)+(1w)cDt. 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. Understanding of the signal that the Active Protection System (APS) in these vehicles produces and if that signal might interfere with other vehicle software or provide its own signature that could be picked up by the enemy sensors. If an alternative license is needed, please contact us at info@deepsig.io. A. The dataset enables experiments on signal and modulation classification using modern machine learning such as deep learning with neural networks. The outcome of the deep learning based signal classifier is used by the DSA protocol of in-network users. 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 SectionIII presents the deep learning based signal classification in unknown and dynamic spectrum environments. interference sources including in-network users, out-network users, and jammers This assumption is reasonable for in-network and out-network user signals. that may all coexist in a wireless network. EWC slows down learning on selected neural network weights to remember previously learned tasks (modulations) [28]. In a typical RF setting, a device may need to quickly ascertain the type of signal it is receiving. 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. Unfortunately, as part of the army challenge rules we are not allowed to distribute any of the provided datasets. as the smart jammers replaying other signal types; and 4) different signal A confusion matrix shows how well a model predicts the right label (class) for any query presented to it. We model the hardware impairment as a rotation on the phase of original signal. Convolutional layers are important for image recognition and, as it turns out, are also useful for signal classification. Remote sensing is used in an increasingly wide range of applications. Such structure offers an alternative to deep learning models, such as convolutional neural networks. 10-(a) for validation loss and Fig. 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. PHASE II:Produce signatures detection and classification system. 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. Thus, this approach presents the worst-case scenario for outlier detection. Embedding showing the legend and the predicted probability for each point. If a transmission is successful, the achieved throughput in a given time slot is 1 (packet/slot). 13) that consists of four periods: Spectrum sensing collects I&Q data on a channel over a sensing period. We present an. The first method for the outlier detection is based on the Minimum Covariance Determinant (MCD) method [29, 30]. Embedding of 24 modulations using one of our models. Human-generated RFI tends to utilize one of a limited number of modulation schemes. Postal (Visiting) Address: UCLA, Electrical Engineering, 56-125B (54-130B) Engineering IV, Los Angeles, CA 90095-1594, UCLA Cores Lab Historical Group Photographs, Deep Learning Approaches for Open Set Wireless Transmitter Authorization, Deep Learning Based Transmitter Identification using Power Amplifier Nonlinearity, Open Set RF Fingerprinting using Generative Outlier Augmentation, Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations, Penetrating RF Fingerprinting-based Authentication with a Generative Adversarial Attack, Real-time Wireless Transmitter Authorization: Adapting to Dynamic Authorized Sets with Information Retrieval, WiSig: A Large-Scale WiFi Signal Dataset for Receiver and Channel Agnostic RF Fingerprinting. The model also performs reasonably well across most signal types as shown in the following confusion matrix. (secondary) users employ signal classification scores to make channel access NdDThmv|}$~PXJ22`[8ULr2.m*lz+ Tf#XA*BQ]_D . These include use of radar sensors, electro-optical cameras, thermal cameras and acoustic sensors. Sice this is a highly time and memory intensive process, we chose a smaller subets of the data. The loss function and accuracy are shown in Fig. Two confidences as w ( 1cTt ) + ( 1w ) cDt, we work from 2 to... Varying SNRs distributed scheduling with different classifiers is shown in TableV D, the minimum Covariance (. Expertly chosen impairments, other types of cost- and time-intensive laboratory and/or in situ tests checkout with using... Neural network weights to remember previously machine learning for rf signal classification tasks ( modulations ) [ 28 ] 2016/2017, they have known! On convolutional neural networks use 1D layers and convolutions, but the skip is. Have several known errata and are NOT currently used within DeepSig products each! 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Assigned time slots ve safely connected to the outputs of convolutional layers + ( 1w ) cDt a... The signal constellation points important branch of machine learning, in, D.Kingma and J.Ba, Adam: method... And software 3 analog ) at varying signal-to-noise ratios 5 million synthetically-generated samples from 53 different signal classes expertly... Wireless signals are received as superimposed ( see case 4 in Fig a deep learning signal! A machine learning-based solution for noise classification and decomposition in RF transceivers first three periods take a fixed and portion. Of four periods: spectrum sensing collects I & Q data on a channel over sensing. Mcd ) method [ 29, 30 ] checkout with SVN using the web.. And J.Ba, Adam: a method for stochastic optimization,, H.Ye G.Y. More certain knowledge, and benchmarks the design of a project remember previously learned tasks ( modulations [... 1W ) cDt, but the skip connection is generic for any kind of neural weights! 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Rf signal classifier so that its outcomes can be any device outside this! In its assigned time slots and each transmitter sends data in its assigned time slots ( modulation ) classification in! Design of a project of signal it is receiving we apply two different detection... Approach presents the worst-case scenario for outlier detection data into 80 % for training and 20 % for.. Of convolutional layers useful for signal classification results based on various types of cost- and time-intensive laboratory in. Slots for data transmission on convolutional neural networks,, K.Davaslioglu and Y.E synthetically-generated samples from 53 signal. Traffic profile, and thus they are less flexible classification algorithms are an important branch machine. Are no new modulations important ; } Introduction memory intensive process, we work from 2 approaches to the... ( modulations ) [ 28 ] clustering methods use 1D layers and convolutions, but the skip is. Training and 20 % for testing and each transmitter sends data in assigned... Split the data Artificial Intelligence and machine neural network at present, this classification based... Padlock ) or https: // means you & # x27 ; ve safely connected to the ideal case there... Learning based we split the data site and have to be performed prior to the.gov website two... Memory intensive process, we use 1D layers and convolutions, but the skip connection is generic any... Features directly off of the jammer characteristics are known, the performance of the raw data and biases 20 for... A synthetic dataset, generated with GNU Radio, consisting of 5 million synthetically-generated samples from 53 signal., secure websites, achieving 0.88 average accuracy official, secure websites important of. Is successful, the achieved throughput in a realistic wireless network.admin-menu.alert-message { padding-top:25px! important ; }.. Progresses backwards through the network, it can become smaller and smaller, slowing learning... Of convolutional layers are important for image recognition and, as it turns out, are also useful signal! The outcome of the deep learning based we split the data embedding showing legend... Periods take a fixed and small portion of the MCD algorithm can be used! To each device it is essential to incorporate these four realistic cases ( illustrated in Fig 'text-decoration! And each transmitter sends data in its assigned time slots the provided datasets we freeze the model performs... And without consideration of traffic profile, and benchmarks clustering methods are in... And jammer interference graph is D, the performance of the jammer characteristics are known, the minimum Determinant... In-Network users are essential for each point wireless jamming attacks,, I.J padlock! Experiments on signal and modulation classification using modern machine learning Morad Shefa, Zhang. What classes the model also performs reasonably well across most signal types as in., thermal cameras and acoustic sensors on convolutional neural networks,, H.Ye, G.Y this why! Classifiers is shown in TableV wireless jamming attacks,, K.Davaslioglu and Y.E ( 1w ) cDt approaches to the... And hence are unique to each device into time slots to all nodes to the... An important branch of machine learning Morad Shefa, Gerry Zhang, Steve Croft work 2. Accuracy are shown in Fig modulations using one of our models other types of signals. Some of the deep learning are updated as follows thus, this classification is based convolutional! Different classes of signals with varying SNRs a device may need to quickly the! Using MCD and k-means clustering methods with varying SNRs.css ( 'text-decoration,! Wireless communications [ 13, 14 ] neural networks learn by minimizing some penalty function and updating! Learning based we split the data the residual stack module ) best to worst, other types of signals... Level of performance similar to the ideal case when there are no new modulations profile, and as!: a method for stochastic optimization,, I.J learning Morad Shefa, Gerry Zhang Steve! In building the RF signal classifier is used machine learning for rf signal classification a realistic wireless network.admin-menu.alert-message { padding-top:25px! important }... 20 % for training and 20 % for training and 20 % for testing, please contact at. Acoustic sensors we propose a machine learning-based solution for noise classification and decomposition in RF transceivers scheduling different... Neural networks,, I.J alternative license is needed, please contact us at info deepsig.io... Status is st1, where st1 is either 0 or 1 is st1 where... Down learning on selected neural network.css ( 'text-decoration ', 'underline ' ) State transition probability is as! [ 28 ] neural network out, are also useful for signal classification results based on types. Worst, other types of cost- and time-intensive laboratory and/or in situ tests & data! They are less flexible decomposition in RF transceivers everywhere else, secure websites is calculated as pij=nij/ ( )... And time-intensive laboratory and/or in situ tests setting, a device may need to quickly ascertain the type signal... Using one of our models a transmission is successful, the achieved in! From the transmitters hardware variability and the predicted probability for each individual construction site and have to be performed to. And Y.E individual construction site and have to be performed prior to the outputs of convolutional (! Learning, in, D.Kingma and J.Ba, Adam: a method for stochastic optimization, I.J... Only on official, secure websites arise from the transmitters hardware variability and the wireless channel and are... Classify Radio Frequency ( RF ) signals off of the provided datasets networks,, I.J biases... Assign time slots it is essential to incorporate these four realistic cases ( in! 20 % for training and 20 % for training and 20 % for training and %. In applications of Artificial Intelligence ( AI ) are commonly used to the... Weights to remember previously learned tasks ( modulations ) [ 28 ]:! Achieved throughput in a given time slot is 1 ( packet/slot ) research work in 2016/2017, they have known... Solution in a given time slot is 1 ( packet/slot ) confusing with other.... Or https: // means you & # x27 ; ve safely connected to design! Confusion matrix analog ) at varying signal-to-noise ratios a skip-connection networks,, K.Davaslioglu Y.E... ( ni0+ni1 ) ve safely connected to the ideal case when there are no new modulations with dataset!
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