model, in, A.Ali and Y. signal (modulation) classification solution in a realistic wireless network 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. 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. Deep learning provides a hands-off approach that allows us to automatically learn important features directly off of the raw data. their actual bandwidths) are centered at 0 Hz (+- random frequency offset, see below), SNR values: 25, 20, 15, 10, 5, 0, -5, -10 dB (AWGN), fading channel: Watterson Model as defined by CCIR 520. In case 1, we applied continual learning to mitigate catastrophic forgetting. They merely represent the space found by t-SNE in which close points in high dimension stay close in lower dimension. Examples of how information can be transmitted by changing the shape of a carrier wave. This assumption is reasonable for in-network and out-network user signals. Instead of using a conventional feature extraction or off-the-shelf deep neural network architectures such as ResNet, we build a custom deep neural network that takes I/Q data as input. An outlier detection is needed as a robust way of detecting if the (jamming) signal is known or unknown. Performance of modulation classification for real RF signals, in, Y.Shi, K.Davaslioglu, and Y.E. Sagduyu, Generative adversarial network for sTt=sDt. based loss. Therefore, we organized a Special Issue on remote sensing . 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 xZ[s~#U%^'rR[@Q z l3Kg~{C_dl./[$^vqW\/n.c/2K=`7tZ;(U]J;F{ u~_: g#kYlF6u$pzB]k:6y_5e6/xa5fuq),|1gj:E^2~0E=? Zx*t :a%? Update these numbers based on past state i and current predicted state j, i.e., nij=nij+1. Here is the ResNet architecture that I reproduced: Notice a few things about the architecture: Skip connections are very simple to implement in Keras (a Python neural network API) and we will talk about this more in my next blog. (MCD) and k-means clustering methods. SectionIII presents the deep learning based signal classification in unknown and dynamic spectrum environments. It accomplishes this by a simple architectural enhancement called a skip-connection. 13) that consists of four periods: Spectrum sensing collects I&Q data on a channel over a sensing period. In my last blog I briefly introduced traditional radio signal classification methods; a meticulous process that required expertly handcrafted feature extractors. Scheduling decisions are made using deep learning classification results. 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. This represents a cleaner and more normalized version of the 2016.04C dataset, which this supersedes. These soil investigations are essential for each individual construction site and have to be performed prior to the design of a project. classification using deep learning model,, T.OShea, T.Roy, and T.C. Clancy, Over-the-air deep learning based radio From best to worst, other types of received signals are ordered as idle, in-network, and jammer. Introduction. Then the signals are cut into short slices. 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. Then a classifier built on known signals cannot accurately detect a jamming signal. Rukshan Pramoditha. 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. .css('color', '#1b1e29') We generate another instance with p00=p11=0.8 and p01=p10=0.2. If the signal is unknown, then users can record it and exchange the newly discovered label with each other. In Fig. Handbook of Anomaly Detection: With Python Outlier Detection (9) LOF. The evaluation settings are as the following: Inlier signals: QPSK, 8PSK, CPFSK, AM-SSB, AM-DSB, GFSK, Outlier signals: QAM16, QAM64, PAM4, WBFM. Unfortunately, as part of the army challenge rules we are not allowed to distribute any of the provided datasets. 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. 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 implementation will also output signal descriptors which may assist a human in signal classification e.g. The VGG and ResNet performances with respect to accuracy are virtually identical until SNR values exceed 10dB, at which point ResNet is the clear winner. DeepSig's team has created several small example datasets which were used in early research from the team in modulation recognition - these are made available here for historical and educational usage. These t-SNE plots helped us to evaluate our models on unlabelled test data that was distributed differently than training data. AbstractIn recent years, Deep Learning (DL) has been successfully applied to detect and classify Radio Frequency (RF) Signals. .css('background', '#FBD04A') The ResNet model showed near perfect classification accuracy on the high SNR dataset, ultimately outperforming both the VGG architecture and baseline approach. In each epoch the network predicts the labels in a feed forward manner. Towards Data Science. Adversarial deep learning for cognitive radio security: Jamming attack and The benchmark performances are given as follows. Results for one of our models without hierarchical inference. In the feature extraction step, we freeze the model in the classifier and reuse the convolutional layers. If the in-network user classifies the received signals as out-network, it does not access the channel. M.Ring, Continual learning in reinforcement environments, Ph.D. 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 The jammer uses these signals for jamming. train a 121 layer deep ResNet with 220,000 trainable parameters on a dataset of two-million signals. Higher values on the Fisher diagonal elements Fi indicate more certain knowledge, and thus they are less flexible. You signed in with another tab or window. A.Odena, V.Dumoulin, and C.Olah, Deconvolution and checkerboard Then based on traffic profile, the confidence of sTt=0 is 1cTt while based on deep learning, the confidence of sDt=0 is cDt. The classification accuracy for inliers and outliers as a function of contamination factor in MCD is shown in Fig. Classification of shortwave radio signals with deep learning, RF Training Data Generation for Machine Learning, Each signal vector has 2048 complex IQ samples with fs = 6 kHz (duration is 340 ms), The signals (resp. At each SNR, there are 1000samples from each modulation type. We use a weight parameter w[0,1] to combine these two confidences as wcTt+(1w)(1cDt). modulation classification for cognitive radio, in, S.Peng, H.Jiang, H.Wang, H.Alwageed, and Y.D. Yao, Modulation Satellite. The confusion matrix is shown in Fig. 10-(a) for validation loss and Fig. .css('display', 'flex') These modulations are categorized into signal types as discussed before. 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. Deep learning (DL) models are the most widely researched AI-based models because of their effectiveness and high performance. 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. 1) and should be classified as specified signal types. Re-training the model using all eight modulations brings several issues regarding memory, computation, and security as follows. to use Codespaces. In case 3, we identified the spoofing signals by extending the CNN structure to capture phase shift due to radio hardware effects. We extend the CNN structure to capture phase shift due to radio hardware effects to identify the spoofing signals and relabel them as jammers. Superposition of jamming and out-network user signals. All datasets provided by Deepsig Inc. are licensed under the Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License (CC BY-NC-SA 4.0). 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. We studied deep learning based signal classification for wireless networks in presence of out-network users and jammers. The Army has invested in development of some training data sets for development of ML based signal classifiers. Here on Medium, we discuss the applications of this tech through our blogs. A. Are you sure you want to create this branch? 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. in. .css('display', 'inline-block') 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. }); We first consider the basic setting that there are no outliers (unknown signal types) and no superimposed signals, and traffic profile is not considered. defense strategies, in, Y.E. Sagduyu, Y.Shi, and T.Erpek, IoT network security from the PHASE II:Produce signatures detection and classification system. This is called the vanishing gradient problem which gets worse as we add more layers to a neural network. this site are copies from the various SBIR agency solicitations and are not necessarily 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. This approach achieves 0.837 average accuracy. 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. OBJECTIVE:Develop and demonstrate a signatures detection and classification system for Army tactical vehicles, to reduce cognitive burden on Army signals analysts. 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. 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). This is why it is called a confusion matrix: it shows what classes the model is confusing with other classes. In-network users that classify received signals to better signal types gain access to channel. The self-generated data includes both real signals (over the air) and synthetic signal data with added noise to model real conditions. We consider the superframe structure (shown in Fig. 1300 17th Street North, Suite 1260 Arlington, VA, 22209, Over-the-air deep learning based radio signal classification, (Warning! 2) Develop open set classification approaches which can distinguish between authorized transmitters and malicious transmitters. Also, you can reach me at moradshefa@berkeley.edu. Fig. We tried two approaches: i) directly apply outlier detection using MCD and ii) extract features and apply MCD outlier detection to these features. 110 0 obj To try out the new user experience, visit the beta website at https://beta.www.sbir.gov/