machine learning for rf signal classification

recognition networks, in, B.Kim, J.K. amd H. Chaeabd D.Yoon, and J.W. Choi, Deep neural Recent advances in machine learning (ML) may be applicable to this problem space. Comment * document.getElementById("comment").setAttribute( "id", "a920bfc3cf160080aec82e5009029974" );document.getElementById("a893d6b3a7").setAttribute( "id", "comment" ); Save my name, email, and website in this browser for the next time I comment. Notice that the VGG and ResNet deep learning approaches show vast improvements in classification accuracy for lower value SNR signals when compared to the baseline model. .css('text-align', 'center') signal (modulation) classification solution in a realistic wireless network classification,, This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. We are trying to build different machine learning models to solve the Signal Modulation Classification problem. There are several potential uses of artificial intelligence (AI) and machine learning (ML) in next-generation shared spectrum systems. Deep learning provides a score on the confidence of classification to four types of signals: idle, in-network, jammer, and out-network. We apply EWC to address this problem. For case 1, we apply continual learning and train a jQuery('.alert-icon') Please reference this page or our relevant academic papers when using these datasets. Training happens over several epochs on the training data. 11.Using image data, predict the gender and age range of an individual in Python. This protocol is distributed and only requires in-network users to exchange information with their neighbors. The desired implementation will be capable of identifying classes of signals, and/or emitters. jQuery('.alert-link') We considered the effect of no jamming and obtained benchmark performance: Benchmark scheme 1: In-network throughput is 881. 10-(a) for validation loss and Fig. Dean, M.Devin, If nothing happens, download Xcode and try again. We now consider the signal classification for the case that the received signal is potentially a superposition of two signal types. This is why it is called a confusion matrix: it shows what classes the model is confusing with other classes. Each layer of a neural net is a mathematical function that transforms its input into a set of features. transmissions. 110 0 obj . throughput and out-network user success ratio. It accomplishes this by a simple architectural enhancement called a skip-connection. If a transmission is successful, the achieved throughput in a given time slot is 1 (packet/slot). 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). Deliver a prototype system to CERDEC for further testing. .admin-menu.alert-message { padding-top:25px !important;} .css('color', '#1b1e29') 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. There is no expert feature extraction or pre-processing performed on the raw data. Then based on traffic profile, the confidence of sTt=0 is 1cTt while based on deep learning, the confidence of sDt=0 is cDt. 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. 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 << /Filter /FlateDecode /Length 4380 >> AbstractIn recent years, Deep Learning (DL) has been successfully applied to detect and classify Radio Frequency (RF) Signals. modulation type, and bandwidth. 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) . 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. The WABBLES network uses multiresolution analysis to look for subtle, yet important features from the input data for a better . If the received signal is classified as in-network, the in-network user needs to share the spectrum with other in-network user(s) based on the confidence of its classification. (Warning! The model also performs reasonably well across most signal types as shown in the following confusion matrix. based loss. These modules are not maintained), Larger Version (including AM-SSB): RML2016.10b.tar.bz2, Example ClassifierJupyter Notebook: RML2016.10a_VTCNN2_example.ipynb. If nothing happens, download GitHub Desktop and try again. 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 first apply blind source separation using ICA. Note that state 0 needs to be classified as idle, in-network, or jammer based on deep learning. 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. sensor networks: Algorithms, strategies, and applications,, M.Chen, U.Challita, W.Saad, C.Yin, and M.Debbah, Machine learning for 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. mitigating wireless jamming attacks,, H.Ye, G.Y. Li, and B.H. Juang, Power of deep learning for channel Feroz, N., Ahad, M.A., Doja, F. Machine learning techniques for improved breast cancer detection and prognosisA comparative analysis. 1, ) such that there is no available training data for supervised learning. If the in-network user classifies the received signals as out-network, it does not access the channel. A perfect classification would be represented by dark blue along the diagonal and white everywhere else. CNN models to solve Automatic Modulation Classification problem. A.Odena, V.Dumoulin, and C.Olah, Deconvolution and checkerboard k-means method can successfully classify all inliers and most of outliers, achieving 0.88 average accuracy. covariance determinant estimator,, Virginia Polytechnic Institute and State University, DeepWiFi: Cognitive WiFi with Deep Learning, The Importance of Being Earnest: Performance of Modulation Suppose the last status is st1, where st1 is either 0 or 1. In this study, computer-aided diagnosis (CAD) systems were used to improve the image quality of mammography images and to detect suspicious areas. Therefore, we organized a Special Issue on remote sensing . 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. At its most simple level, the network learns a function that takes a radio signal as input and spits out a list of classification probabilities as output. This dataset was first released at the 6th Annual GNU Radio Conference. We combine these two confidences as w(1cTt)+(1w)cDt. Compared with benchmark A CNN structure similar to the one in SectionIII-A is used. Benchmark scheme 1: In-network user throughput is 829. var warning_html = '

SBIR.gov is getting modernized! Convolutional layers are important for image recognition and, as it turns out, are also useful for signal classification. We have the following benchmark performance. The average accuracy over all signal-to-noise-ratios (SNRs) is 0.934. 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. The best contamination factor is 0.15, which maximizes the minimum accuracy for inliers and outliers. Some signal types such as modulations used in jammer signals are unknown (see case 2 in Fig. 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). 1.1. networks, in, J.Kirkpatrick, R.Pascanu, N.Rabinowitz, J.Veness, G.Desjardins, A. 1) in building the RF signal classifier so that its outcomes can be practically used in a DSA protocol. EWC slows down learning on selected neural network weights to remember previously learned tasks (modulations) [28]. The axis have no physical meaning. This process generates data, that is close to real reception signals. At present, this classification is based on various types of cost- and time-intensive laboratory and/or in situ tests. Dimensionality reduction after extracting features of 16PSK (red), 2FSK_5kHz (green),AM_DSB (blue). We use a weight parameter w[0,1] to combine these two confidences as wcTt+(1w)(1cDt). The authors note that no significant training improvement is seen from increasing the dataset from one-million examples to two-million examples. The first method for the outlier detection is based on the Minimum Covariance Determinant (MCD) method [29, 30]. We apply blind source separation using Independent Component Analysis (ICA) [9] to obtain each single signal that is further classified by deep learning. 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. For case 2, we detect unknown signals via outlier detection applied Unlike the signal strength and carrier sense time, the PDR is calculated in a sliding window, that is, the packet delivery rate is updated once a packet is successfully received. Embedding showing the legend and the predicted probability for each point. 12, respectively. .css('justify-content', 'center') TDMA-based schemes, we show that distributed scheduling constructed upon signal 2 out-network users and 2 jammers are randomly distributed in the same region. These modules are not maintained), Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License. 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. We consider the superframe structure (shown in Fig. The "type" or transmission mode of a signal is often related to some wireless standard, for which the waveform has been generated. 1) and should be classified as specified signal types. Classification, Distributive Dynamic Spectrum Access through Deep Reinforcement The rest of the paper is organized as follows. RF is an ensemble machine learning algorithm that is employed to perform classification and regression tasks . A clean signal will have a high SNR and a noisy signal will have a low SNR. We split the data into 80% for training and 20% for testing. decisions and share the spectrum with each other while avoiding interference 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. To support dynamic spectrum access (DSA), in-network users need to sense the spectrum and characterize interference sources hidden in spectrum dynamics. However, these two approaches require expert design or knowledge of the signal. Then the jammer amplifies and forwards it for jamming. Vadum is seeking a Signal Processing Engineer/Scientist to develop machine learning and complex signal processing algorithms. If you are trying to listen to your friend in a conversation but are having trouble hearing them because of a lawn mower running outside, that is noise. However, we will provide: Simple embedding of our small mnist model (no legend, no prediction probability). RF-Signal-Model. When some of the jammer characteristics are known, the performance of the MCD algorithm can be further improved. It is essential to incorporate these four realistic cases (illustrated in Fig. Signal Generation Software: https://github.com/radioML/dataset Warning! SectionIV introduces the distributed scheduling protocol as an application of deep learning based spectrum analysis. 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 Then based on traffic profile, the confidence of sTt=0 is cTt while based on deep learning, the confidence of sDt=1 is 1cDt. 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. modulation type, and bandwidth. 2018: Disease Detection: EMG Signal Classification for Detecting . They merely represent the space found by t-SNE in which close points in high dimension stay close in lower dimension. SectionII discusses related work. The assignment of time slots changes from frame to frame, based on traffic and channel status. Also, you can reach me at moradshefa@berkeley.edu. 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. There are different reasons why signal modulation classification can be important. defense strategies, in, Y.E. Sagduyu, Y.Shi, and T.Erpek, IoT network security from the Satellite. 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). classification using convolutional neural network based deep learning 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.
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