machine learning for rf signal classification

The desired implementation will be capable of identifying classes of signals, and/or emitters. Therefore, we organized a Special Issue on remote sensing . The classification accuracy for inliers and outliers as a function of contamination factor in MCD is shown in Fig. signal sources. Background dissertation, University of Texas at Austin, 1994. If you are interested in learning more about DeepSig and our solutions, contact us! In case 1, we applied continual learning to mitigate catastrophic forgetting. Deep learning based signal classifier determines channel status based on sensing results. Improved CNN model for RadioML dataset signals are superimposed due to the interference effects from concurrent transmissions of different signal types. .admin-menu.alert-message { padding-top:25px !important;} Are you sure you want to create this branch? We introduce the Sig53 dataset consisting of 5 million synthetically-generated samples from 53 different signal classes and expertly chosen impairments. We present an. Your email address will not be published. RF-Signal-Model. They report seeing diminishing returns after about six residual stacks. Benchmark scheme 1: In-network throughput is 760. We propose a machine learning-based solution for noise classification and decomposition in RF transceivers. classification using convolutional neural network based deep learning The rest of the paper is organized as follows. sign in 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. 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. SectionII discusses related work. network-based automatic modulation classification technique, in, G.J. Mendis, J.Wei, and A.Madanayake, Deep learning-based automated The paper proposes using a residual neural network (ResNet) to overcome the vanishing gradient problem. We recommend researchers and ML engineers create their own datasets using real data for new work and usage! In my last blog I briefly introduced traditional radio signal classification methods; a meticulous process that required expertly handcrafted feature extractors. 1) and should be classified as specified signal types. State transition probability is calculated as pij=nij/(ni0+ni1). Some signal types such as modulations used in jammer signals are unknown (see case 2 in Fig. All datasets provided by Deepsig Inc. are licensed under the Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License (CC BY-NC-SA 4.0). Signal Modulation Classification Using Machine Learning, Datasets provided by the Army Rapid Capabilities Offices Artificial Intelligence Signal Classification challenge, Simulated signals of 24 different modulations: 16PSK, 2FSK_5KHz, 2FSK_75KHz, 8PSK, AM_DSB, AM_SSB, APSK16_c34, APSK32_c34, BPSK, CPFSK_5KHz, CPFSK_75KHz, FM_NB, FM_WB, GFSK_5KHz, GFSK_75KHz, GMSK, MSK, NOISE, OQPSK, PI4QPSK, QAM16, QAM32, QAM64, QPSK, 6 different signal to noise ratios (SNR): -10 dB, -6 dB, -2 dB, 2 dB, 6 dB, 10 dB, Used deep convolutional neural networks for classification, CNNs are widely used and have advanced performance in computer vision, Convolutions with learned filters are used to extract features in the data, Hierarchical classification: Classify into subgroups then use another classifier to identify modulation, Data augmentation: Perturbing the data during training to avoid overfit, Ensemble training: Train multiple models and average predictions, Residual Connections: Allow for deeper networks by avoiding vanishing gradients, Layers with filters of different dimensions, Extracting output of final inception layer; 100 per modulation (dimension: 5120), Reducing dimension using principal component analysis (dimension: 50), Reducing dimension using t-distributed neighbor embedding (dimension: 2), The ability of CNNs to classify signal modulations at high accuracy shows great promise in the future of using CNNs and other machine learning methods to classify RFI, Future work can focus on extending these methods to classify modulations in real data, One can use machine learning methods to extend these models to real data, Use domain adaptation to find performing model for a target distribution that is different from the source distribution/ training data, a notebook that we used to experiment with different models and that is able to achieve The deep learning method relies on stochastic gradient descent to optimize large parametric neural network models. We now consider the signal classification for the case that the received signal is potentially a superposition of two signal types. .css('color', '#1b1e29') We combine these two confidences as w(1cTt)+(1w)cDt. The authors note that no significant training improvement is seen from increasing the dataset from one-million examples to two-million examples. 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. Also, you can reach me at moradshefa@berkeley.edu. random phase offset. In this section, we present a distributed scheduling protocol that makes channel access decisions to adapt to dynamics of interference sources along with channel and traffic effects. 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. 7 So innovative combination of SVD imaging markers and clinical predictors using different ML algorithms such as random forest (RF) and eXtreme Gradient Boosting . Demonstrate such a system. A. Dobre, A.Abdi, Y.Bar-Ness, and W.Su, Survey of automatic modulation 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. As instrumentation expands beyond frequencies allocated to radio astronomy and human generated technology fills more of the wireless spectrum classifying RFI as such becomes more important. 10-(a) for validation loss and Fig. 1I}3'3ON }@w+ Q8iA}#RffQTaqSH&8R,fSS$%TOp(e affswO_d_kgWVv{EmUl|mhsB"[pBSFWyDrC 2)t= t0G?w+omv A+W055fw[ 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). 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. Signal classification is an important functionality for cognitive radio applications to improve situational awareness (such as identifying interference sources) and support DSA. those with radiation Dose > 0 versus 0). throughput and out-network user success ratio. 1, ) such that there is no available training data for supervised learning. 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). sTt=sDt. In our second approach, we converted the given data set into spectrogram images of size 41px x 108px and ran CNN models on the image data set. It is essential to incorporate these four realistic cases (illustrated in Fig. The performance measures are in-network user throughput (packet/slot) and out-network user success ratio (%). jQuery("header").prepend(warning_html); S.i.Amari, A.Cichocki, and H.H. Yang, A new learning algorithm for blind some signal types are not known a priori and therefore there is no training data available for those signals; signals are potentially spoofed, e.g., a smart jammer may replay received signals from other users thereby hiding its identity; and. For example, radio-frequency interference (RFI) is a major problem in radio astronomy. 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. BOTH | 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. However, an intruder can be any device outside of this set. Dynamic spectrum access (DSA) benefits from detection and classification of TDMA-based schemes, we show that distributed scheduling constructed upon signal The desired implementation will be capable of identifying classes of signals, and/or emitters. The subsets chosen are: The results of the model are shown below: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. .css('align-items', 'center') The benchmark performances are given as follows. based loss. 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. Component Analysis (ICA) to separate interfering signals. A deep convolutional neural network architecture is used for signal modulation classification. Benchmark scheme 1. wireless networks with artificial intelligence: A tutorial on neural The Army has invested in development of some training data sets for development of ML based signal classifiers. 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. We use patience of 8 epochs (i.e., if loss at epoch t did not improve for 8 epochs, we stop and take the best (t8) result) and train for 200 iterations. Out-network user success is 47.57%. This method divides the samples into k=2 clusters by iteratively finding k cluster centers. in. 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. For case 2, we detect unknown signals via outlier detection applied 1) if transmitted at the same time (on the same frequency). 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. Fan, Unsupervised feature learning and automatic modulation 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). . This RF signal dataset contains radio signals of 18 different waveforms for the training of machine learning systems. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Then the signals are cut into short slices. This calls for passive physical layer based authentication methods that use the transmitters RF fingerprint without any additional overhead on the transmitters. The implementation will also output signal descriptors which may assist a human in signal classification e.g. 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. We start with the simple baseline scenario that all signal types (i.e., modulations) are fixed and known (such that training data are available) and there are no superimposed signals (i.e., signals are already separated). Compared with benchmark A.Odena, V.Dumoulin, and C.Olah, Deconvolution and checkerboard 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. Then based on traffic profile, the confidence of sTt=0 is cTt while based on deep learning, the confidence of sDt=1 is 1cDt. The file is formatted as a "pickle" file which can be opened for example in Python by using cPickle.load(). with out-network (primary) users and jammers. classification using deep learning model,, T.OShea, T.Roy, and T.C. Clancy, Over-the-air deep learning based radio classification results provides major improvements to in-network user It turns out you can use state of the art machine learning for this type of classification. By utilizing the signal classification results, we constructed a distributed scheduling protocol, where in-network (secondary) users share the spectrum with each other while avoiding interference imposed to out-network (primary) users and received from jammers. empirical investigation of catastrophic forgetting in gradient-based neural Embedding showing the legend and the predicted probability for each point. In the past few years deep learning models have out-paced traditional methods in computer vision that, like the current state of signal classification, involved meticulously creating hand-crafted feature extractors. US ground force tactical Signals Intelligence (SIGINT) and EW sensors require the ability to rapidly scan large swaths of the RF spectrum and automatically characterize emissions by frequency and. The point over which we hover is labelled 1 with predicted probability 0.822. Modulation Classification, {http://distill.pub/2016/deconv-checkerboard/}. OBJECTIVE:Develop and demonstrate a signatures detection and classification system for Army tactical vehicles, to reduce cognitive burden on Army signals analysts. we used ns-3 to simulate different jamming techniques on wireless . Out-network user success is 16%. decisions and share the spectrum with each other while avoiding interference In this project our objective are as follows: 1) Develop RF fingerprinting datasets. Suppose the current classification by deep learning is sDt with confidence cDt, where sDt is either 0 or 1 and cDt is in [0.5,1]. AQR: Machine Learning Related Research Papers Recommendation, fast.ai Tabular DataClassification with Entity Embedding, Walk through TIMEPart-2 (Modelling of Time Series Analysis in Python). CNN models to solve Automatic Modulation Classification problem. 2 out-network users and 2 jammers are randomly distributed in the same region. Adversarial deep learning for cognitive radio security: Jamming attack and Embedding of 24 modulations using one of our models. 2) Develop open set classification approaches which can distinguish between authorized transmitters and malicious transmitters. We present a deep learning based Each slice is impaired by Gaussian noise, Watterson fading (to account for ionospheric propagation) and random frequency and phase offset. https://www.acq.osd.mil/osbp/sbir/solicitations/index.shtml. train a 121 layer deep ResNet with 220,000 trainable parameters on a dataset of two-million signals. Sice this is a highly time and memory intensive process, we chose a smaller subets of the data. recognition networks, in, B.Kim, J.K. amd H. Chaeabd D.Yoon, and J.W. Choi, Deep neural I/Q data is a translation of amplitude and phase data from a polar coordinate system to a cartesian coordinate system. To try out the new user experience, visit the beta website at https://beta.www.sbir.gov/