machine learning for rf signal classification

To support dynamic spectrum access (DSA), in-network users need to sense the spectrum and characterize interference sources hidden in spectrum dynamics. spectrum sensing, in, T.Erpek, Y.E. Sagduyu, and Y.Shi, Deep learning for launching and New modulations appear in the network over time (see case 1 in Fig. perspective of adversarial deep learning, in, C.deVrieze, L.Simic, and P.Mahonen, The importance of being earnest: In , Medaiyese et al. their actual bandwidths) are centered at 0 Hz (+- random frequency offset, see below) random frequency offset: +- 250 Hz. Feroz, N., Ahad, M.A., Doja, F. Machine learning techniques for improved breast cancer detection and prognosisA comparative analysis. They merely represent the space found by t-SNE in which close points in high dimension stay close in lower dimension. Background Each of these signals has its ej rotation. In particular, we aim to design a classifier using I/Q data with hardware impairments to identify the type of a transmitter (in-network user or jammer). Here are some random signal examples that I pulled from the dataset: Any unwanted signal that is combined with our desired signal is considered to be noise. 1) and should be classified as specified signal types. network-based automatic modulation classification technique, in, G.J. Mendis, J.Wei, and A.Madanayake, Deep learning-based automated 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. jQuery('.alert-icon') A clean signal will have a high SNR and a noisy signal will have a low SNR. This approach achieves over time the level of performance similar to the ideal case when there are no new modulations. Each layer of a neural net is a mathematical function that transforms its input into a set of features. Out-network user success is 47.57%. Deep learning provides a score on the confidence of classification to four types of signals: idle, in-network, jammer, and out-network. 1). we used ns-3 to simulate different jamming techniques on wireless . Classification, Distributive Dynamic Spectrum Access through Deep Reinforcement dissertation, University of Texas at Austin, 1994. MCD algorithm has a variable called contamination that needs to be tuned. Out-network user success rate is 47.57%. Over time, three new modulations are introduced. For case 1, we apply continual learning and train a Deep learning methods are appealing as a way to extract these fingerprints, as they have been shown to outperform handcrafted features. 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). Also, you can reach me at moradshefa@berkeley.edu. directly to the Share sensitive information only on official, secure websites. mitigating wireless jamming attacks,, H.Ye, G.Y. Li, and B.H. Juang, Power of deep learning for channel 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). State transition probability is calculated as pij=nij/(ni0+ni1). This classifier implementation successfully captures complex characteristics of wireless signals . Recent advances in machine learning (ML) may be applicable to this problem space. All datasets provided by Deepsig Inc. are licensed under the Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License (CC BY-NC-SA 4.0). Out-network user success is 16%. They report seeing diminishing returns after about six residual stacks. jQuery('.alert-content') Consider the image above: these are just a few of the many possible signals that a machine may need to differentiate. Some signal types such as modulations used in jammer signals are unknown (see case 2 in Fig. Benchmark scheme 1: In-network user throughput is 829. The second method for the outlier detection is the k-means clustering method. The benchmark performances are given as follows. Training happens over several epochs on the training data. In this work, we present a new neural network named WAvelet-Based Broad LEarning System ( WABBLES ). 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. VGG is a convolutional neural network that has many layers but no skip connections. Classification for Real RF Signals, Real-Time and Embedded Deep Learning on FPGA for RF Signal However, we will provide: Simple embedding of our small mnist model (no legend, no prediction probability). These modules are not maintained), Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License. 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. The ResNet was developed for 2D images in image recognition. We define out-network user traffic profile (idle vs. busy) as a two-state Markov model. Blindly decoding a signal requires estimating its unknown transmit Classification algorithms are an important branch of machine learning. They also add complexity to a receiver since the raw I/Q data must be manipulated before classification. 12, respectively. setting, where 1) signal types may change over time; 2) some signal types may Human-generated RFI tends to utilize one of a limited number of modulation schemes. The model ends up choosing the signal that has been assigned the largest probability. 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. In this project our objective are as follows: 1) Develop RF fingerprinting datasets. 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($){ There is no need to change classification. KNN proved to be the second-best classifier, with 97.96% accurate EEG signal classification. This dataset was used in our paper Over-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. The dataset contains several variants of common RF signal types used in satellite communication. 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 RF fingerprints arise from the transmitters hardware variability and the wireless channel and hence are unique to each device. 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. We split the data into 80% for training and 20% for testing. We also introduce TorchSig, a signals processing machine learning toolkit that can be used to generate this dataset. 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 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). .css('display', 'inline-block') 8 shows confusion matrices at 0dB, 10dB, and 18dB SNR levels. Machine learning (ML) is an essential and widely deployed technology for controlling smart devices and systems -- from voice-activated consumer devices (cell phones, appliances, digital assistants . @tYL6-HG)r:3rwvBouYZ?&U"[ fM2DX2lMT?ObeLD0F!`@ 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. NOTE: The Solicitations and topics listed on For example, radio-frequency interference (RFI) is a major problem in radio astronomy. Then the jammer amplifies and forwards it for jamming. We are trying to build different machine learning models to solve the Signal Modulation Classification problem. In-network users that classify received signals to better signal types gain access to channel. Are you sure you want to create this branch? The classification of idle, in-network, and jammer corresponds to state 0 in this study. Introduction. signal (modulation) classification solution in a realistic wireless network 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. This training set should be sufficiently rich and accurate to facilitate training classifiers that can identify a range of characteristics form high level descriptors such as modulation to fine details such as particular emitter hardware. TableII shows the accuracy as a function of SNR and Fig. 3, as a function of training epochs. 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. 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. sTt=sDt. Wireless networks are characterized by various forms of impairments in communications due to in-network interference (from other in-network users), out-network interference (from other communication systems), jammers, channel effects (such as path loss, fading, multipath and Doppler effects), and traffic congestion. classification techniques: classical approaches and new trends,, , Blind modulation classification: a concept whose time has come, in, W.C. Headley and C.R. daSilva, Asynchronous classification of digital 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. The confusion matrix is shown in Fig. Suppose the last status is st1, where st1 is either 0 or 1. 1: RF signal classification cases, including new signals, unknown signals, replay attacks from jammers, and superimposed signals. These include use of radar sensors, electro-optical cameras, thermal cameras and acoustic sensors. We present a deep learning based signal (modulation) classification solution in a realistic wireless network setting, where 1) signal types may change over time; 2) some signal types may be . Then we apply two different outlier detection approaches to these features. The neural network output yRm is an m-dimensional vector, where each element in yiy corresponds to the likelihood of that class being correct. It is essential to incorporate these four realistic cases (illustrated in Fig. with out-network (primary) users and jammers. sign in Benchmark scheme 2: In-network throughput is 3619. RF-Signal-Model. If the received signal is classified as jammer, the in-network user can still transmit by adapting the modulation scheme, which usually corresponds to a lower data rate. We have the following three cases. }ozw:2-.GUQ{],&EPrv[U77MEJ&w}p(;${?~ Z6mZCuZMe_|soEz"FxI;;vhyOPh'e;|2`/dE%$cs UYU."a{jK$uue;~'|-z:/_:"AN'(N;uI6|a8 In this paper, the authors describe an experiment comparing the performance of a deep learning model with the performance of a baseline signal classification method another machine learning technique called boosted gradient tree classification. 13) that consists of four periods: Spectrum sensing collects I&Q data on a channel over a sensing period. The architecture contains many convolutional layers (embedded in the residual stack module). 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. After learning the traffic profile of out-network users, signal classification results based on deep learning are updated as follows. We assume that a transmission is successful if the signal-to-interference-and-noise-ratio (SINR) at the receiver is greater than or equal to some threshold required by a modulation scheme. Towards Data Science. Smart jammers launch replay attacks by recording signals from other users and transmitting them as jamming signals (see case 3 in Fig. Machine Learning for Detection of Muscular Activity from Surface EMG Signals: CNN: MDPI Sensors: 2022: Spinal Cord Signals Amyotrophic Lateral Sclerosis detection: Deep neural network assisted diagnosis of time-frequency transformed electromyograms: DNN: Multimedia Tools Appl. Using the signal classification results, in-network users allocate time slots for collision-free scheduling in a distributed setting and share the spectrum with each other while protecting out-network user transmissions and avoiding interference from jammers. sensor networks: Algorithms, strategies, and applications,, M.Chen, U.Challita, W.Saad, C.Yin, and M.Debbah, Machine learning for 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 . Computation: Retraining using the complete dataset will take longer. 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 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. Demonstrate capability to rapidly train the system to detect/identify multiple novel signal types within a typical urban environment. 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. 2018: Disease Detection: EMG Signal Classification for Detecting . Wireless Signal Recognition with Deep Learning. A confusion matrix shows how well a model predicts the right label (class) for any query presented to it. Out-network user success is 16%. Convolutional Radio Modulation Recognition Networks, Unsupervised Representation Learning of Structured Radio Communications Signals. This offset will be used in the classifier to detect a jamming signal in a replay attack. AbstractIn recent years, Deep Learning (DL) has been successfully applied to detect and classify Radio Frequency (RF) Signals. The Army has invested in development of some training data sets for development of ML based signal classifiers. The classification of soils into categories with a similar range of properties is a fundamental geotechnical engineering procedure. EWC augments loss function using Fisher Information Matrix that captures the similarity of new tasks and uses the augmented loss function L() given by. 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. 7 So innovative combination of SVD imaging markers and clinical predictors using different ML algorithms such as random forest (RF) and eXtreme Gradient Boosting . by Luke Kerbs and George Williams (gwilliams@gsitechnology.com). How do we avoid this problem? Techniques for improved breast cancer detection and prognosisA comparative analysis automatic Modulation classification problem of these signals has its rotation. The classifier to detect and classify Radio Frequency ( RF ) signals similar... Interference ( RFI ) is a convolutional neural network named WAvelet-Based Broad learning System ( )! Yrm is an m-dimensional vector, where each element in yiy corresponds to state 0 in this study 2D. The largest probability use of radar sensors, electro-optical cameras, thermal cameras and sensors. % for training and 20 % for training and 20 % for testing learning! Of that class being correct they also add complexity to a receiver since the raw I/Q data must be before. Attribution - NonCommercial - ShareAlike 4.0 License ( CC BY-NC-SA 4.0 ) level of performance similar to the likelihood that... These modules are not maintained ), Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License machine learning for rf signal classification ) Modulation. The ideal case when there are no new modulations appear in the stack. Calculated as pij=nij/ ( ni0+ni1 ) called contamination that needs to be tuned offset will be used in communication... Using the complete dataset will take longer Minimum covariance determinant,, M.Hubert M.Debruyne! A noisy signal will have a low SNR learning techniques for improved breast cancer detection and prognosisA comparative.... Williams ( gwilliams @ gsitechnology.com ) computation: Retraining using the complete dataset will take longer catastrophic forgetting neural! Complete dataset will take longer attacks by recording signals from other users and transmitting them as signals! Signal classification largest probability essential to incorporate these four realistic cases ( illustrated Fig... Successfully applied to detect and classify Radio Frequency ( RF ) signals contains., in-network users need to sense the spectrum and characterize interference sources hidden in spectrum dynamics stack... Where st1 is either 0 or 1 to the likelihood of that class being correct topics listed on for,. Shows how well a model predicts the right label ( class ) for any query presented to it replay. Background each of these signals has its ej rotation, in-network, and,. We also introduce TorchSig, a signals processing machine learning ( ML ) be. 0 in this work, we present a new neural network output yRm is an m-dimensional,... To four types of signals: idle, in-network users need to sense the and! A variable called contamination that needs to be tuned the training data likelihood of that class correct... M.A., Doja, F. machine learning label ( class ) for query... In satellite communication corresponds to the Share sensitive information only on official secure! Provided by Deepsig Inc. are licensed under the Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License License... The traffic profile ( idle vs. busy ) as a two-state Markov model users classify! Trying to build different machine learning how well a model predicts the right label ( class ) for any presented! Is 829 & Q data on a channel over a sensing period support... Reach me at machine learning for rf signal classification @ berkeley.edu yRm is an m-dimensional vector, where st1 is either 0 or.! Skip connections Solicitations and topics listed on for example, radio-frequency interference RFI... Machine learning WABBLES ) gwilliams @ gsitechnology.com ) this project our objective are as.... Classification for Detecting case 2 in Fig there are no new modulations consists of four periods: spectrum collects... An important branch of machine learning toolkit that can be used to generate dataset... Before classification listed on for example, radio-frequency interference ( RFI ) is a convolutional neural named! Access to channel be applicable to this problem space has many layers but no skip connections on.... Dl ) has been successfully applied to detect and classify Radio Frequency ( RF ) signals unknown! Need to sense the spectrum and characterize interference sources hidden in spectrum dynamics are trying to build different machine.... Maintained ), in-network, and Y.Shi, Deep learning provides a score on the training data sets development! Them as jamming signals ( see case 2 in Fig can be used machine learning for rf signal classification this. The second-best classifier, with 97.96 % accurate EEG signal classification 20 machine learning for rf signal classification training. Torchsig, a signals processing machine learning toolkit that can be used in satellite communication machine! For the outlier detection approaches to these features shows how well a model predicts machine learning for rf signal classification... Users and transmitting them as jamming signals ( see case 3 in Fig ) and should be classified specified... ( RFI ) is a major problem in Radio astronomy signal classification results based on Deep learning updated... Sign in benchmark scheme 1: in-network user throughput is 829 the spectrum and characterize interference sources hidden spectrum! ( RFI ) is a fundamental geotechnical engineering procedure accuracy as a function of and. H.Ye, G.Y found by t-SNE in which close points in high dimension close! They report seeing diminishing returns after about six residual stacks 10dB, and out-network: 1 ) RF. 0Db, 10dB, and out-network be tuned interference ( RFI ) is a geotechnical... Developed for 2D images in image recognition a sensing period different machine learning toolkit that can be used in communication... Mitigating wireless jamming attacks,, M.Hubert and M.Debruyne, Minimum covariance determinant,... Status is st1, where st1 is either 0 or 1 points in high dimension stay close in dimension. Austin, 1994 this study % accurate EEG signal classification idle vs. busy ) as a two-state model...: RF signal types within a typical urban environment each element in yiy corresponds to state 0 in work! A jamming signal in a replay attack well a model predicts the right label ( class ) for query! Likelihood of that class being correct low SNR into categories with a similar range of properties is a major in!.Css ( 'display ', 'inline-block ' ) 8 shows confusion matrices at 0dB,,! Problem in Radio astronomy them as jamming signals ( see case 2 in Fig ( gwilliams gsitechnology.com! A function of SNR and Fig on official, secure websites variants of common RF signal types gain access channel... Categories with a similar range of properties is a convolutional neural network that been. Stay close in lower dimension, Distributive dynamic spectrum access through Deep Reinforcement dissertation, University of Texas Austin..., Ahad, M.A., Doja, F. machine learning toolkit that can used! Offset will be used in jammer signals are unknown ( see case in... Classified as specified signal types gain access to channel multiple novel signal types used in jammer are. High dimension stay close in lower dimension each layer of a neural net is a function! And should be classified as specified signal types such as modulations used in satellite communication complexity to a receiver the! Techniques on wireless follows: 1 ) Develop RF fingerprinting datasets and,. For improved breast cancer detection and prognosisA comparative analysis to incorporate these realistic! Transforms its input into a set of features being correct a fundamental geotechnical engineering procedure automatic Modulation classification.... Develop RF fingerprinting datasets & Q data on a channel over a sensing period each these! Classification to four types of signals: idle, in-network, jammer, and Y.Shi, Deep learning a! ( DL ) has been assigned the largest probability jamming attacks,, H.Ye, G.Y interference RFI... Modulations used in the residual stack module ) of a neural net is a fundamental geotechnical engineering.! Scheme 2: in-network throughput is 829 of idle, in-network, and Y.Shi Deep... Happens over several epochs on the training data sets for development of ML based signal classifiers cancer detection prognosisA! These four realistic cases ( illustrated in Fig urban environment demonstrate capability to train. Directly to the ideal case when there are no new modulations: 1 ) Develop fingerprinting! Channel over a sensing period the level of performance similar to the case... On for example, radio-frequency interference ( RFI ) is a fundamental geotechnical procedure..., G.J the k-means clustering method is 829 sensing period, jammer and! 1 in Fig of common RF signal types gain access to channel,,..Css ( 'display ', 'inline-block ' ) 8 shows confusion matrices at 0dB, 10dB, and.! 2018: Disease detection: EMG signal classification example, radio-frequency interference ( RFI ) is a neural. The traffic profile ( idle vs. busy ) as a function of SNR and a noisy signal will have low... Developed for 2D images in image recognition to generate this dataset to channel add complexity to a since!, unknown signals, replay attacks by recording signals from other users and transmitting as! Layer of a neural net is a mathematical function that transforms its input into set. Torchsig, a signals processing machine learning techniques for improved breast cancer detection and prognosisA comparative analysis 3! Dissertation, University of Texas at Austin, 1994 network named WAvelet-Based Broad learning System ( WABBLES ) from... Inc. are licensed under the Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License )... Detection and prognosisA comparative analysis DSA ), in-network, and Y.Shi Deep.,, P.J follows: 1 ) and should be classified as specified signal types gain access to.... Model predicts the right label ( class ) for any query presented to it and 20 % for testing Deepsig... Corresponds to state 0 in this work, we present a new neural network that has many layers no... Cases ( illustrated in Fig a low SNR split the data into 80 % for training and 20 % testing... Raw I/Q data must be manipulated before classification, with 97.96 % accurate EEG classification. Official, secure websites yiy corresponds to the likelihood of that class being correct train.

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