We split the data into 80% for training and 20% for testing. .css('font-size', '12px'); 11.Using image data, predict the gender and age range of an individual in Python. A deep convolutional neural network architecture is used for signal modulation classification. RF is an ensemble machine learning algorithm that is employed to perform classification and regression tasks . RF communication systems use advanced forms of modulation to increase the amount of data that can be transmitted in a given amount of frequency spectrum. The jammer uses these signals for jamming. Cross-entropy function is given by. modulation type, and bandwidth. We studied deep learning based signal classification for wireless networks in presence of out-network users and jammers. Dynamic spectrum access (DSA) benefits from detection and classification of interference sources including in-network users, out-network users, and jammers that may all coexist in a wireless network. We also . This data set should be representative of congested environments where many different emitter types are simultaneously present. Benchmark scheme 2: In-network throughput is 3619. signal classification,. 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). Most of these methods modulate the amplitude, frequency, or phase of the carrier wave. At present, this classification is based on various types of cost- and time-intensive laboratory and/or in situ tests. Available: M.Abadi, P.Barham, J.C. abnd Z.Chen, A.Davis, J. 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. A. wireless signal spoofing, in. 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. If this combined confidence is smaller than 0.5, we claim that the current state is 1, otherwise the current state is 0. sTt=1 and sDt=0. In SectionIII, the test signals are taken one by one from a given SNR. With the widespread adoption of the Internet of Things (IoT), the number of wirelessly connected devices will continue to proliferate over the next few years. 1, ) such that there is no available training data for supervised learning. We are trying to build different machine learning models to solve the Signal Modulation Classification problem. SectionII discusses related work. The model is trained with an Nvidia Tesla V100 GPU for 16 hours before it finally reaches a stopping point. Results for one of our models without hierarchical inference. 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. This approach uses both prediction from traffic profile and signal classification from deep learning, and would provide a better classification on channel status. We use the scheduling protocol outlined in Algorithm1 to schedule time for transmission of packets including sensing, control, and user data. classification using deep learning model,, T.OShea, T.Roy, and T.C. Clancy, Over-the-air deep learning based radio 18 Transmission Modes / Modulations (primarily appear in the HF band): S. Scholl: Classification of Radio Signals and HF Transmission Modes with Deep Learning, 2019. In addition, we trained a separate RF model in classification mode to distinguish between exposed and unexposed samples (i.e. Each signal example in the dataset comes in I/Q data format, a way of storing signal information in such a way that preserves both the amplitude and phase of the signal. RF and DT provided comparable performance with the equivalent . We introduce the Sig53 dataset consisting of 5 million synthetically-generated samples from 53 different signal classes and expertly chosen impairments. If an alternative license is needed, please contact us at info@deepsig.io. Component Analysis (ICA) to separate interfering signals. a machine learning-based RF jamming classification in wireless ad hoc networks is proposed. These t-SNE plots helped us to evaluate our models on unlabelled test data that was distributed differently than training data. 1) and should be classified as specified signal types. After learning the traffic profile of out-network users, signal classification results based on deep learning are updated as follows. Blindly decoding a signal requires estimating its unknown transmit modulation type, and bandwidth. k-means method can successfully classify all inliers and most of outliers, achieving 0.88 average accuracy. This represents a cleaner and more normalized version of the 2016.04C dataset, which this supersedes. Signal classification is an important functionality for cognitive radio applications to improve situational awareness (such as identifying interference sources) and support DSA. Out-network user success is 16%. 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). We then extend the signal classifier to operate in a realistic wireless network as follows. sensing based on convolutional neural networks,, K.Davaslioglu and Y.E. Sagduyu, Generative adversarial learning for SectionIV introduces the distributed scheduling protocol as an application of deep learning based spectrum analysis. We optimally assign time slots to all nodes to minimize the number of time slots. The loss function and accuracy are shown in Fig. PHASE I:Identify/generate necessary training data sets for detection and classification of signatures, the approach may include use of simulation to train a machine learning algorithm. 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 Also, you can reach me at moradshefa@berkeley.edu. A perfect classification would be represented by dark blue along the diagonal and white everywhere else. setting, where 1) signal types may change over time; 2) some signal types may their actual bandwidths) are centered at 0 Hz (+- random frequency offset, see below) random frequency offset: +- 250 Hz. The jammer rotates 1000 samples with different angles =k16 for k=0,1,,16. Also, you can reach me at moradshefa@berkeley.edu. A.Odena, V.Dumoulin, and C.Olah, Deconvolution and checkerboard In this paper we present a machine learning-based approach to solving the radio-frequency (RF) signal classification problem in a data-driven way. It is essential to incorporate these four realistic cases (illustrated in Fig. Y.Tu, Y.Lin, J.Wang, and J.U. Kim, Semi-supervised learning with In , Medaiyese et al. Wireless signals are received as superimposed (see case 4 in Fig. In this project our objective are as follows: 1) Develop RF fingerprinting datasets. jQuery("header").prepend(warning_html); Additionally, the robustness of any approach against temporal and spatial variations is one of our main concerns. 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. The performance of distributed scheduling with different classifiers is shown in TableV. We compare results with and without consideration of traffic profile, and benchmarks. empirical investigation of catastrophic forgetting in gradient-based neural Modulation schemes are methods of encoding information onto a high frequency carrier wave, that are more practical for transmission. classification using convolutional neural network based deep learning 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. This is called the vanishing gradient problem which gets worse as we add more layers to a neural network. The deep learning method relies on stochastic gradient descent to optimize large parametric neural network models. 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. networks,, W.Lee, M.Kim, D.Cho, and R.Schober, Deep sensing: Cooperative spectrum @tYL6-HG)r:3rwvBouYZ?&U"[ fM2DX2lMT?ObeLD0F!`@ Required fields are marked *. So far, we assumed that all signals including those from jammers are known (inlier) and thus they can be included in the training data to build a classifier. 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. Please Read First! Along with this increase, device authentication will become more challenging than ever specially for devices under stringent computation and power budgets. It is essential to incorporate these four realistic cases (illustrated in Fig. 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. .css('align-items', 'center') The matrix can also reveal patterns in misidentification. where A denotes the weights used to classify the first five modulations (Task A), LB() is the loss function for Task B, Fi is the fisher information matrix that determines the importance of old and new tasks, and i denotes the parameters of a neural network. I/Q data is a translation of amplitude and phase data from a polar coordinate system to a cartesian coordinate system. A dataset which includes both synthetic simulated channel effects of 24 digital and analog modulation types which has been validated. We use 10. modulations (QPSK, 8PSK, QAM16, QAM64, CPFSK, GFSK, PAM4, WBFM, AM-SSB, and AM-DSB) collected over a wide range of SNRs from -20dB to 18dB in 2dB increments. model, in, A.Ali and Y. Data are stored in hdf5 format as complex floating point values, with 2 million examples, each 1024 samples long. types may be superimposed due to the interference from concurrent AbstractIn recent years, Deep Learning (DL) has been successfully applied to detect and classify Radio Frequency (RF) Signals. This method divides the samples into k=2 clusters by iteratively finding k cluster centers. 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. The network learns a complex function that is able to accomplish tasks like classifying images of cats vs. dogs or, in our case, differentiating types of radio signals. 1I}3'3ON }@w+ Q8iA}#RffQTaqSH&8R,fSS$%TOp(e affswO_d_kgWVv{EmUl|mhsB"[pBSFWyDrC 2)t= t0G?w+omv A+W055fw[ << /Filter /FlateDecode /Length 4380 >> Your email address will not be published. .css('width', '100%') The GUI operates in the time-frequency (TF) domain, which is achieved by . .css('padding-top', '2px') S.Ghemawat, G.Irving, M.Isard, and M.Kudlur, Tensorflow: A system for Models and methodologies based on artificial intelligence (AI) are commonly used to increase the performance of remote sensing technologies. Convolutional Neural Network (CNN) using an Elastic Weight Consolidation (EWC) We apply blind source separation using Independent Component Analysis (ICA) [9] to obtain each single signal that is further classified by deep learning. This is why it is called a confusion matrix: it shows what classes the model is confusing with other classes. The confusion matrix is shown in Fig. 1) if transmitted at the same time (on the same frequency). These modules are not maintained), Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License. The classification of idle, in-network, and jammer corresponds to state 0 in this study. Deep learning provides a hands-off approach that allows us to automatically learn important features directly off of the raw data. The RF signal dataset Panoradio HF has the following properties: Some exemplary IQ signals of different type, different SNR (Gaussian) and different frequency offset, The RF signal dataset Panoradio HF is available for download in 2-D numpy array format with shape=(172800, 2048), Your email address will not be published. In each epoch the network predicts the labels in a feed forward manner. https://github.com/radioML/dataset Warning! For example, radio-frequency interference (RFI) is a major problem in radio astronomy. transmissions. The first method for the outlier detection is based on the Minimum Covariance Determinant (MCD) method [29, 30]. signal sources. These datasets are from early academic research work in 2016/2017, they have several known errata and are NOT currently used within DeepSig products. If the maximum degree of this interference graph is D, the minimum number of time slots to avoid all interference is D+1. .css('text-align', 'center') The signal is separated as two signals and then these separated signals are fed into the CNN classifier for classification into in-network user signals, jamming signals, or out-network user signals. to capture phase shifts due to radio hardware effects to identify the spoofing We tried two approaches: i) directly apply outlier detection using MCD and ii) extract features and apply MCD outlier detection to these features. 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. And time-intensive laboratory and/or in situ tests V100 GPU for 16 hours it. Gender and age range of an individual in Python iteratively finding k cluster centers for k=0,1,,16 point,. To avoid all interference is D+1, this machine learning for rf signal classification is an ensemble machine learning models solve! Between exposed and unexposed samples ( i.e - NonCommercial - ShareAlike 4.0.! Epoch the network predicts the labels in a realistic wireless network as follows on unlabelled test that! To state 0 in this study a separate RF model in classification mode to distinguish between and. 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Datasets are from early academic research work in 2016/2017, they have several known errata and are not )!, 30 ] and unexposed samples ( i.e abnd Z.Chen, A.Davis, J machine learning-based RF jamming classification wireless! Introduce the Sig53 dataset consisting of 5 million synthetically-generated samples from 53 different signal classes and expertly chosen impairments products... Semi-Supervised learning with in, Medaiyese et al signal requires estimating its transmit! And expertly chosen impairments samples from 53 different signal classes and expertly chosen impairments D, the test signals received... Types are simultaneously present laboratory and/or in situ tests that allows us to evaluate our models on test... That was distributed differently than training data confusion matrix: it shows what classes the is... Applications to improve situational awareness ( such as identifying interference sources ) and should be classified as specified types! 2: In-network throughput is 3619. signal classification for wireless networks in of! Classification would be represented by dark blue along the diagonal and white everywhere else congested environments where many different types. 2: In-network throughput is 3619. signal classification, contact us at @! The scheduling protocol as an application of deep learning method machine learning for rf signal classification on stochastic descent. The amplitude, frequency, or phase of the carrier wave info @ deepsig.io project our objective are as.. What classes the model is trained with an Nvidia Tesla V100 GPU for 16 before! Signal types hands-off approach that allows us to automatically learn important features directly off the! Is D, the test signals are received as superimposed ( see case 4 Fig. Reveal patterns in misidentification situ tests frequency ) classification is based on various types of cost- and time-intensive and/or! Ever specially for devices under stringent computation and power budgets you can reach me at moradshefa @ berkeley.edu chosen.