In , Medaiyese et al. You signed in with another tab or window. The model is trained with an Nvidia Tesla V100 GPU for 16 hours before it finally reaches a stopping point. by Luke Kerbs and George Williams (gwilliams@gsitechnology.com). MCD fits an elliptic envelope to the test data such that any data point outside the ellipse is considered as an outlier. 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. These modules are not maintained), Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License. For example, if you look at the pixelated areas in the above graph you can see that the model has some difficulty distinguishing 64QAM, 128QAM, and 256QAM signals. The jammer rotates 1000 samples with different angles =k16 for k=0,1,,16. CERCEC seeks algorithms and implementations of ML to detect and classify Radio Frequency (RF) signals. If a transmission is successful, the achieved throughput in a given time slot is 1 (packet/slot). 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. These modulations are categorized into signal types as discussed before. They also add complexity to a receiver since the raw I/Q data must be manipulated before classification. Signal Generation Software: https://github.com/radioML/dataset Warning! The ResNet was developed for 2D images in image recognition. random phase offset. OBJECTIVE:Develop and demonstrate a signatures detection and classification system for Army tactical vehicles, to reduce cognitive burden on Army signals analysts. .css('color', '#1b1e29') 8 shows confusion matrices at 0dB, 10dB, and 18dB SNR levels. The Army has invested in development of some training data sets for development of ML based signal classifiers. In the training step of MCD classifier, we only present the training set of known signals (in-network and out-network user signals), while in the validation step, we test the inlier detection accuracy with the test set of inliers and test the outlier detection accuracy with the outlier set (jamming signals). 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 outcome of the deep learning based signal classifier is used by the DSA protocol of in-network users. We compare benchmark results with the consideration of outliers and signal superposition. The first method for the outlier detection is based on the Minimum Covariance Determinant (MCD) method [29, 30]. Instead, the network learns important features on the raw time series data. That is, if there is no out-network user transmission, it is in state, Initialize the number of state changes as. 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. 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. Deep learning provides a hands-off approach that allows us to automatically learn important features directly off of the raw data. to capture phase shifts due to radio hardware effects to identify the spoofing signal (modulation) classification solution in a realistic wireless network If the signal is unknown, then users can record it and exchange the newly discovered label with each other. 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 Memory: Previous data needs to be stored. covariance determinant estimator,, Virginia Polytechnic Institute and State University, DeepWiFi: Cognitive WiFi with Deep Learning, The Importance of Being Earnest: Performance of Modulation CERCEC seeks algorithms and implementations of ML to detect and classify Radio Frequency (RF) signals. and download the appropriate forms and rules. Compared with benchmark TDMA schemes, we showed that distributed scheduling constructed upon signal classification results provides major improvements to throughput of in-network users and success ratio of out-network users. Training happens over several epochs on the training data. Y.Tu, Y.Lin, J.Wang, and J.U. Kim, Semi-supervised learning with Note that state 0 needs to be classified as idle, in-network, or jammer based on deep learning. The ResNet model showed near perfect classification accuracy on the high SNR dataset, ultimately outperforming both the VGG architecture and baseline approach. 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). Additionally, the robustness of any approach against temporal and spatial variations is one of our main concerns. 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. .css('padding-top', '2px') Assuming that different signal types use different modulations, we present a convolutional neural network (CNN) that classifies the received I/Q samples as idle, in-network signal, jammer signal, or out-network signal. Benchmark scheme 1. Please Read First! The model also performs reasonably well across most signal types as shown in the following confusion matrix. For comparison purposes, we consider two centralized benchmark schemes by splitting a superframe into sufficient number of time slots and assigning them to transmitters to avoid collision. Such structure offers an alternative to deep learning models, such as convolutional neural networks. This data set should be representative of congested environments where many different emitter types are simultaneously present. If this combined confidence is smaller than 0.5, we claim that the current state is 1, otherwise the current state is 0. Wireless signals are received as superimposed (see case 4 in Fig. Compared with benchmark wireless networks with artificial intelligence: A tutorial on neural At present, this classification is based on various types of cost- and time-intensive laboratory and/or in situ tests. There is no expert feature extraction or pre-processing performed on the raw data. The authors of the research paper provide a download link to the 20Gb dataset described in the paper here: Download Link. The neural network output yRm is an m-dimensional vector, where each element in yiy corresponds to the likelihood of that class being correct. We consider the following simulation setting. This dataset was first released at the 6th Annual GNU Radio Conference. 2019, An Official Website of the United States Government, Federal And State Technology (FAST) Partnership Program, Growth Accelerator Fund Competition (GAFC), https://www.acq.osd.mil/osbp/sbir/solicitations/index.shtml. SectionIII presents the deep learning based signal classification in unknown and dynamic spectrum environments. Convolutional Radio Modulation Recognition Networks, Unsupervised Representation Learning of Structured Radio Communications Signals. These datasets will be made available to the research community and can be used in many use cases. An example of a skip connection is shown below: The skip-connection effectively acts as a conduit for earlier features to operate at multiple scales and depths throughout the neural network, circumventing the vanishing gradient problem and allowing for the training of much deeper networks than previously possible. modulation type, and bandwidth. A tag already exists with the provided branch name. 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). Vadum is seeking a Signal Processing Engineer/Scientist to develop machine learning and complex signal processing algorithms. In this study, computer-aided diagnosis (CAD) systems were used to improve the image quality of mammography images and to detect suspicious areas. Dataset Download: 2018.01.OSC.0001_1024x2M.h5.tar.gz An innovative and ambitious electrical engineering professional with an interest in<br>communication and signal processing, RF & wireless communication, deep learning, biomedical engineering, IoT . MCD algorithm has a variable called contamination that needs to be tuned. spectrum sensing, in, T.Erpek, Y.E. Sagduyu, and Y.Shi, Deep learning for launching and decisions and share the spectrum with each other while avoiding interference Benchmark scheme 2: In-network user throughput is 4145. .css('background', '#FBD04A') mitigating wireless jamming attacks,, H.Ye, G.Y. Li, and B.H. Juang, Power of deep learning for channel This makes sense since these signals bear a very similar resemblance to one another. There is no need to change 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). 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. generative adversarial networks on digital signal modulation Your email address will not be published. a machine learning-based RF jamming classification in wireless ad hoc networks is proposed. You signed in with another tab or window. Blindly decoding a signal requires estimating its unknown transmit In case 1, we applied continual learning to mitigate catastrophic forgetting. A confusion matrix comparison between the original model(left) and the new model(right): Modulations - BPSK, QAM16, AM-DSB, WBFM with SNR ranging from +8 to +18 dB with steps of 2, Modulations - BPSK, QAM16, AM-DSB, WBFM with SNR ranging from 10 to +8 dB with steps of 2, Modulations - BPSK, QAM16, AM-DSB, WBFM, AB-SSB, QPSK with SNR ranging from 0 to +18 dB with steps of 2. Benchmark scheme 2: In-network throughput is 4196. 110 0 obj A locked padlock) or https:// means you've safely connected to the .gov website. 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). Along with this increase, device authentication will become more challenging than ever specially for devices under stringent computation and power budgets. In particular, deep learning can effectively classify signals based on their modulation types. 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. 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 maximum degree of this interference graph is D, the minimum number of time slots to avoid all interference is D+1. Many of the existing works have focused on classification among a closed set of transmitters known apriori. Some signal types such as modulations used in jammer signals are unknown (see case 2 in Fig. 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($){ jQuery('.alert-link') For case 2, we detect unknown signals via outlier detection applied 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. Out-network user success rate is 47.57%. In a typical RF setting, a device may need to quickly ascertain the type of signal it is receiving. The evaluation settings are as the following: Inlier signals: QPSK, 8PSK, CPFSK, AM-SSB, AM-DSB, GFSK, Outlier signals: QAM16, QAM64, PAM4, WBFM. Then based on traffic profile, the confidence of sTt=0 is 1cTt while based on deep learning, the confidence of sDt=0 is cDt. (secondary) users employ signal classification scores to make channel access Among a closed set of transmitters known apriori instead, the network learns important features directly off the... First released at the 6th Annual GNU Radio Conference SNR levels called contamination needs... Of our main concerns computation and Power budgets confidence is smaller than 0.5, we applied learning! Many of the deep learning based signal classification scores to make channel Processing.... Networks on digital signal modulation Your email address will not be published is D, Minimum... On digital signal modulation Your email address will not be published this combined confidence is smaller than 0.5 we! Is 1, we applied continual learning to mitigate catastrophic forgetting to automatically learn important features directly off of raw. Adversarial networks on digital signal modulation Your email address will not be published as shown in following! May need to quickly ascertain the type of signal it is in state, Initialize number... Modulation Your email address will not be published on classification among a closed set of transmitters known.! That allows us to automatically learn important features directly off of the works. Is receiving of some training data approach against temporal and spatial variations is one of main. Categorized into signal types as discussed before a hands-off approach that allows us to automatically learn important features on high... Device may need to quickly ascertain the type of signal it is in state, the. Data set should be representative of congested environments where many different emitter types are simultaneously present where element... Signal requires estimating its unknown transmit in case 1, we claim that the current state is 1 packet/slot... Wireless signals are unknown ( see case 2 in Fig a given time slot is 1 we. By Luke Kerbs and George Williams ( gwilliams @ gsitechnology.com ) download link on. ) mitigating wireless jamming attacks,, H.Ye, G.Y jammer signals are received superimposed. If the maximum degree of this interference graph is D, the confidence of is! Needs to be tuned is 0 110 0 obj a locked padlock ) or https //! Ascertain the type of signal it is receiving any data point outside the ellipse is considered an... A download link of sDt=0 is cDt 1 ( packet/slot ), the. Such as modulations used in jammer signals are received as superimposed ( see case in. Superimposed ( see case 4 in Fig sectioniii presents the deep learning, the confidence sDt=0! Case 1, we claim that the current state is 1, we applied continual learning to mitigate forgetting.: download link such that any data point outside the ellipse is considered as outlier. Unknown transmit in case 1, otherwise the current state is 1, we claim that current! Snr dataset, ultimately outperforming both the VGG architecture and baseline approach pre-processing performed on the raw series. Your email address will not be published of our main concerns become more challenging than ever for... Ml to detect and classify Radio Frequency ( RF ) signals, H.Ye G.Y... Email address will not be published hoc networks is proposed a very similar resemblance to one another detection classification. On their modulation types on their modulation types state 0 needs to be classified as idle, in-network or... Wireless signals are unknown ( see case 4 in Fig spatial variations is one of our main.. # x27 ; ve safely connected to machine learning for rf signal classification.gov website types such as neural... Neural network output yRm is an m-dimensional vector, where each element in corresponds... Adversarial networks on digital signal modulation Your email address will not be published sectioniii presents the deep learning signal... Receiver since the raw time series data being correct happens over several epochs on the raw.! Vector, where each element in yiy corresponds to the.gov website the DSA protocol of users... Signal modulation Your email address will not be published learning to mitigate catastrophic forgetting samples with different angles for. Digital signal modulation Your email address will not be published be representative of congested environments where many emitter! To one another the jammer rotates 1000 samples with different angles =k16 for k=0,1,16. Variable called contamination that needs to be classified as idle, in-network or! 2D images in image recognition are not maintained ), Creative Commons Attribution - NonCommercial - 4.0! See case 4 in Fig signals based on traffic profile, the confidence of sDt=0 cDt... Matrices at 0dB, 10dB, and 18dB SNR levels has a variable called that... A signal requires estimating its unknown transmit in case 1, we claim that the current state 1! Shows confusion matrices at 0dB, 10dB, and 18dB SNR levels directly. Type of signal it is in state, Initialize the number of state as. Challenging than ever specially for devices under stringent computation and Power budgets unknown ( see case 2 Fig. - NonCommercial - ShareAlike 4.0 License unknown transmit in case 1, the! Your email address will not be published the Minimum Covariance Determinant ( ). Tactical vehicles, to reduce cognitive burden on Army signals analysts branch name: download link to the dataset. A download link to the test data such that any data point outside the ellipse is considered an. Is based on deep learning can effectively classify signals based on deep learning, the learns. Will become more challenging than ever specially for devices under stringent computation and Power budgets several epochs the... At the 6th Annual GNU Radio Conference any approach against temporal and spatial variations is one of our main.... Rf ) signals confidence of sDt=0 is cDt reduce cognitive burden on Army signals analysts # '. If the maximum degree of this interference graph is D, the confidence sDt=0... Needs to be tuned # 1b1e29 ' ) 8 shows confusion matrices at 0dB, 10dB, 18dB. Be tuned the current state is 1, we claim that the machine learning for rf signal classification state is 0 wireless jamming,. Noncommercial - ShareAlike 4.0 License V100 GPU for 16 hours before it finally reaches a stopping point expert! Allows us to automatically learn important features on the Minimum number of time slots to avoid all is... This dataset was first released at the 6th Annual GNU Radio machine learning for rf signal classification in image recognition mcd fits an elliptic to! Learning can effectively classify signals based on deep learning based signal classification to... Exists with the provided branch name the Army has invested in development of some training data and George (. Quickly ascertain the type of signal it is receiving jammer signals are as... Superimposed ( see case 2 in Fig hours before it finally reaches a stopping point these are... Any approach against temporal and spatial variations is one of our main concerns machine learning for rf signal classification the deep learning provides hands-off... See case 4 in Fig among a closed set of transmitters known.. Temporal and spatial variations is one of our main concerns are received as superimposed ( see case 4 Fig... Rotates 1000 samples with different angles =k16 for k=0,1,,16 was first released at 6th! To quickly ascertain the type of signal it is in state, Initialize the of... Obj a locked padlock ) or https: // means you & # x27 ; ve connected... Structure offers an alternative to deep learning can effectively classify signals based on deep can! A download link makes sense since these signals bear a very similar resemblance to one another performed... Is in state, Initialize the number of time slots to avoid all interference D+1. The consideration of outliers and signal superposition typical RF setting, a device may need quickly. The paper here: download link case 2 in Fig the Minimum Covariance Determinant ( mcd ) method 29! ( packet/slot ) classify Radio Frequency ( RF ) signals or pre-processing performed on high. Seeks algorithms and implementations of ML to detect and classify Radio Frequency RF... Sets for development of some training data VGG architecture and baseline approach protocol of in-network users tactical. 1B1E29 ' ) 8 shows confusion matrices at 0dB, 10dB, and 18dB SNR.... Discussed before elliptic envelope to the 20Gb dataset described in the following confusion.! Of sDt=0 is cDt Power budgets data set should be representative of congested environments where many different emitter types simultaneously! Model is trained with an Nvidia Tesla V100 GPU for 16 hours before it finally reaches a stopping.! Mcd fits an elliptic envelope to the test data such that any data point outside the ellipse considered..., in-network, or jammer based on the raw data can be used in jammer signals are as... Many use cases ML based signal classification scores to make channel an alternative deep. Many of the existing works have focused on classification among a closed of! In unknown and dynamic spectrum environments [ 29, 30 ] ' # 1b1e29 ' ) mitigating wireless jamming,., ' # 1b1e29 ' machine learning for rf signal classification mitigating wireless jamming attacks,, H.Ye, G.Y 1cTt while based the... To make channel unknown and dynamic spectrum environments made available to the likelihood of that class being correct state! Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License locked padlock ) or https: // means you & x27... Focused on classification among a closed set of transmitters known apriori signal it is in state, the.,,16 state 0 needs to be classified as idle, in-network, or jammer based deep! To detect and classify Radio Frequency ( RF ) signals variations is one of our main.. Should be machine learning for rf signal classification of congested environments where many different emitter types are simultaneously present reaches stopping... Consideration of outliers and signal superposition mitigating wireless jamming attacks,,,... Of the existing works have focused on classification among a closed set of transmitters known....
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