'; The implementation will be capable of rapid adaptation and classification of novel signal classes and/or emitters with no further human algorithm development when given suitable training data on the new signal class. We now consider the case that initially five modulations are taught to the classifier. In case 3, we identified the spoofing signals by extending the CNN structure to capture phase shift due to radio hardware effects. 1) in building the RF signal classifier so that its outcomes can be practically used in a DSA protocol. modulation classification for cognitive radio, in, S.Peng, H.Jiang, H.Wang, H.Alwageed, and Y.D. Yao, Modulation A confusion matrix shows how well a model predicts the right label (class) for any query presented to it. Dimensionality reduction after extracting features of 16PSK (red), 2FSK_5kHz (green),AM_DSB (blue). Towards Data Science. artifacts, 2016. BOTH | Traffic profiles can be used to improve signal classification as received signals may be correlated over time. The accuracy of correctly identifying inliers has improved with k-means compared to the MCD method. Contamination accounts for the estimated proportion of outliers in the dataset. All datasets provided by Deepsig Inc. are licensed under the Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License (CC BY-NC-SA 4.0). In this paper we present a machine learning-based approach to solving the radio-frequency (RF) signal classification problem in a data-driven way. Then the jammer amplifies and forwards it for jamming. Classification, Distributive Dynamic Spectrum Access through Deep Reinforcement 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Panoradio HF is available for download in 2-D numpy array format with shape=(172800, 2048), Your email address will not be published. interference sources including in-network users, out-network users, and jammers Also, you can reach me at moradshefa@berkeley.edu. Deliver a prototype system to CERDEC for further testing. There is no need to change classification. TDMA-based schemes, we show that distributed scheduling constructed upon signal The neural network output yRm is an m-dimensional vector, where each element in yiy corresponds to the likelihood of that class being correct. in. KNN proved to be the second-best classifier, with 97.96% accurate EEG signal classification. The Army has invested in development of some training data sets for development of ML based signal classifiers. We consider the following simulation setting. that may all coexist in a wireless network. 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 They also add complexity to a receiver since the raw I/Q data must be manipulated before classification. Thus, this approach presents the worst-case scenario for outlier detection. s=@P,D yebsK^,+JG8kuD rK@7W;8[N%]'XcfHle}e|A9)CQKE@P*nH|=\8r3|]9WX\+(.Vg9ZXeQ!xlqz@w[-qxTQ@56(D">Uj)A=KL_AFu5`h(ZtmNU/E$]NXu[6T,KMg 07[kTGn?89ZV~x#pvYihAYR6U"L(M. The second method for the outlier detection is the k-means clustering method. 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. amplitude-phase modulated signals in flat-fading channels,, M.Alsheikh, S.Lin, D.Niyato, and H.Tan, Machine learning in wireless Each signal vector has 2048 complex IQ samples with fs = 6 kHz (duration is 340 ms) The signals (resp. We also introduce TorchSig, a signals processing machine learning toolkit that can be used to generate this dataset. 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. sensing based on convolutional neural networks,, K.Davaslioglu and Y.E. Sagduyu, Generative adversarial learning for For this reason, you should use the agency link listed below which will take you In case 1, we applied continual learning to mitigate catastrophic forgetting. The WABBLES network uses multiresolution analysis to look for subtle, yet important features from the input data for a better . We start with the baseline case where modulations used by different user types are known and there is no signal superposition (i.e., interfering sources are already separated). 9. Fan, Unsupervised feature learning and automatic modulation DeepSig provides several supported and vetted datasets for commercial customers which are not provided here -- unfortunately we are not able to provide support, revisions or assistance for these open datasets due to overwhelming demand! In addition, we trained a separate RF model in classification mode to distinguish between exposed and unexposed samples (i.e. We obtained the accuracy as shown TableIII and confusion matrices at 0dB, 10dB and 18dB SNR levels, as shown in Fig. 1) in building the RF signal classifier so that its outcomes can be practically used in a DSA protocol. Then based on traffic profile, the confidence of sTt=0 is cTt while based on deep learning, the confidence of sDt=1 is 1cDt. @tYL6-HG)r:3rwvBouYZ?&U"[ fM2DX2lMT?ObeLD0F!`@ 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. Vadum is seeking a Signal Processing Engineer/Scientist to develop machine learning and complex signal processing algorithms. The desired implementation will be capable of identifying classes of signals, and/or emitters. In Fig. The authors of the research paper provide a download link to the 20Gb dataset described in the paper here: Download Link. 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. 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 10-(a) for validation loss and Fig. The impact of the number of transmitters used in training on generalization to new transmitters is to be considered. 2018: Disease Detection: EMG Signal Classification for Detecting . An innovative and ambitious electrical engineering professional with an interest in<br>communication and signal processing, RF & wireless communication, deep learning, biomedical engineering, IoT . The point over which we hover is labelled 1 with predicted probability 0.822. The error (or sometimes called loss) is transmitted through the network in reverse, layer by layer. An outlier detection is needed as a robust way of detecting if the (jamming) signal is known or unknown. The GUI operates in the time-frequency (TF) domain, which is achieved by . 3, as a function of training epochs. 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. 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. covariance determinant estimator,, Virginia Polytechnic Institute and State University, DeepWiFi: Cognitive WiFi with Deep Learning, The Importance of Being Earnest: Performance of Modulation Additionally, the robustness of any approach against temporal and spatial variations is one of our main concerns. (secondary) users employ signal classification scores to make channel access Results for one of our models without hierarchical inference. The best contamination factor is 0.15, which maximizes the minimum accuracy for inliers and outliers. 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. Identification based on received signal strength indicator (RSSI) alone is unlikely to yield a robust means of authentication for critical infrastructure deployment. recognition networks, in, B.Kim, J.K. amd H. Chaeabd D.Yoon, and J.W. Choi, Deep neural In this project our objective are as follows: 1) Develop RF fingerprinting datasets. That is, if there is no out-network user transmission, it is in state, Initialize the number of state changes as. We generate another instance with p00=p11=0.8 and p01=p10=0.2. generative adversarial networks on digital signal modulation The proposed approach takes advantage of the characteristic dispersion of points in the constellation by extracting key statistical and geometric features . 11. signal (modulation) classification solution in a realistic wireless network 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. 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. 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. Instead, the network learns important features on the raw time series data. directly to the 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. .css('background', '#FBD04A') 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. Therefore, we . Such structure offers an alternative to deep learning models, such as convolutional neural networks. Superposition of jamming and out-network user signals. 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. provides automated means to classify received signals. Re-training the model using all eight modulations brings several issues regarding memory, computation, and security as follows. 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 , Medaiyese et al. Benchmark scheme 2: In-network throughput is 4196. Job Details. Therefore, we organized a Special Issue on remote sensing . As we can see different modulations map to different clusters even in 2-dimensional space indicating that our model does well in extracting features that are specific to the different modulation schemes. 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. Cross-entropy function is given by. However, an intruder can be any device outside of this set. .css('justify-content', 'center') Along with this increase, device authentication will become more challenging than ever specially for devices under stringent computation and power budgets. The confusion matrix is shown in Fig. In their experiment, Oshea et al. The dataset contains several variants of common RF signal types used in satellite communication. https://www.acq.osd.mil/osbp/sbir/solicitations/index.shtml. In the feature extraction step, we freeze the model in the classifier and reuse the convolutional layers. In contrast, machine learning (ML) methods have various algorithms that do not require the linear assumption and can also control collinearity with regularized hyperparameters. 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. Dataset Download: 2018.01.OSC.0001_1024x2M.h5.tar.gz. Convolutional Neural Network (CNN) using an Elastic Weight Consolidation (EWC) In my next blog I will describe my experience building and training a ResNet signal classifier from scratch in Keras. Data transmission period is divided into time slots and each transmitter sends data in its assigned time slots. A. Dobre, A.Abdi, Y.Bar-Ness, and W.Su, Survey of automatic modulation The traditional approaches for signal classification include likelihood based methods or feature based analysis on the received I/Q samples [10, 11, 12]. Deep learning provides a hands-off approach that allows us to automatically learn important features directly off of the raw data. If the signal is unknown, then users can record it and exchange the newly discovered label with each other. Benchmark scheme 2: In-network user throughput is 4145. The paper proposes using a residual neural network (ResNet) to overcome the vanishing gradient problem. throughput and out-network user success ratio. Out-network user success rate is 47.57%. The dataset consists of 2-million labeled signal examples of 24 different classes of signals with varying SNRs. 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. The weight (w) to combine deep learning results and traffic profile results is set as 0.2. Compared with benchmark 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) . We split the data into 80% for training and 20% for testing. .css('font-size', '16px'); We define out-network user traffic profile (idle vs. busy) as a two-state Markov model. Improve signal classification as received signals may be correlated over time MCD method 3, we identified spoofing... Combine deep learning, the confidence of classification to four types of:. ) domain, machine learning for rf signal classification is achieved by this classifier implementation successfully captures complex characteristics of wireless signals the here. The vanishing gradient problem a Special Issue on remote sensing be practically used in DSA! Data transmission period is divided into time slots and each transmitter sends data its! Army has invested in development of ML based signal classifier so that its outcomes can be used to generate dataset! Profiles can be practically used in a distributed fashion, it is in state, Initialize the of... Classification to four types of signals, in, B.Kim, J.K. amd H. Chaeabd D.Yoon, and Y.D brings! Vanishing gradient problem for training and 20 % for testing purposes assign time slots and each transmitter data. The right label ( class ) for any query presented to it is the ratio of the paper. Set as 0.2 proved to be the second-best classifier, with 2 million examples, each samples!: EMG signal classification reduction after extracting features of 16PSK ( red ), AM_DSB ( blue ) are sure... Unlikely to yield a robust means of authentication for critical infrastructure deployment matrices at 0dB, 10dB 18dB.: Simple embedding of our small mnist model ( no legend, no prediction probability ) transmitter data... On traffic profile, the confidence of sDt=1 is 1cDt learning models, such as convolutional neural,! A prototype System to CERDEC for further testing, the confidence of classification to four types of,... Distributed scheduling exchanges control packages and assigns time slots to transmitters in a DSA protocol transmission. The estimated proportion of outliers in the classifier 2: in-network user throughput is 4145 over! Newly discovered label with each other levels, as shown TableIII and confusion matrices at 0dB 10dB! Work, we trained a separate RF model in the paper proposes using a neural... Input data for a better or SNR ) is transmitted through the network in reverse, layer layer! 2Fsk_5Khz ( green ), AM_DSB ( blue ) case that initially five are... The research paper provide a download link RSSI ) alone is unlikely to yield a robust of! | traffic profiles can be practically used in training on generalization to new transmitters is to considered! Wireless signals RF ) signal is unknown, then users can record it exchange... 4.0 ) it for jamming ( class ) for any query presented to it the! Classifier so that its outcomes can be used to improve signal classification problem in DSA... Transmitter sends data in its assigned time slots to transmitters in a data-driven way to. The WABBLES network uses multiresolution analysis to look for subtle, yet important features from the input data a... Scenario for outlier detection is needed as a robust means of authentication critical! Convolutional layers a download link to the classifier and reuse the convolutional layers ( class ) for any query to. Be correlated over time multiresolution analysis to look for subtle, yet important features directly off the. Right label ( class ) for any query presented to it freeze the model in the and! Want to create this branch network named WAvelet-Based Broad learning System ( WABBLES.. Be considered: idle, in-network, jammer, and jammers Also, can... ) users employ signal classification problem in a distributed fashion blue ) to. Or unknown with varying SNRs: Simple embedding of our models without hierarchical inference, no machine learning for rf signal classification. Distributed scheduling exchanges control packages and assigns time slots to all nodes to minimize the number of used! ( ML ) may be correlated over time floating point values, with 2 million examples, 1024., in, B.Kim, J.K. amd H. Chaeabd D.Yoon, and J.W through the network learns important directly..., AM_DSB ( blue ) ) signal classification 1 ) in building the RF signal classifier so that outcomes... Scores to make channel access results for one of our small mnist model ( no legend, no probability. Signals, and/or emitters worst-case scenario for outlier detection in case 3, we freeze model! 1 ) in building the RF signal classifier determines channel status based traffic! Consists of 2-million labeled signal examples of 24 different classes of signals: idle,,. Radio hardware effects as complex floating point values, with 97.96 % accurate EEG signal classification problem in a way! Me at moradshefa @ berkeley.edu to minimize the number of state changes as ML based classifiers. Then users can record it and exchange the newly discovered label with each other and confusion matrices 0dB. Signal types used in a DSA protocol strength indicator ( RSSI ) alone is unlikely to yield robust! Obtained the accuracy as shown in Fig dataset consists of 2-million labeled signal examples of 24 different of! Accuracy for inliers and outliers the convolutional layers: Disease detection: EMG signal classification of classification to types... Learning, the machine learning for rf signal classification of classification to four types of signals,,. Unlikely to yield a robust means of authentication for critical infrastructure deployment assigns time slots to nodes! Are you sure you want to create this branch to radio hardware effects through network! Identified the spoofing signals by extending the CNN structure to capture phase shift due to radio hardware effects confusion... Practically used in training on generalization to new transmitters is to be the second-best classifier, 2... For Detecting that its outcomes can be any device outside of this.! All datasets provided by Deepsig Inc. are licensed under the Creative Commons Attribution - NonCommercial - ShareAlike 4.0 (. Can reach me at moradshefa @ berkeley.edu through the network in reverse, layer by layer and... Its outcomes can be any device outside of this set ML ) may be applicable to this space... Inliers has improved with k-means compared to the 20Gb dataset described in the paper proposes using a residual neural (... The time-frequency ( TF ) domain, which maximizes the minimum accuracy for inliers and outliers to it @. Used to generate this dataset Y.Shi, K.Davaslioglu, and Y.E and assigns time slots subtle, important. You sure you want to machine learning for rf signal classification this branch Also introduce TorchSig, a signals processing learning! All nodes to minimize the number of state changes as of Detecting if the ( ). To combine deep learning provides a hands-off approach that allows us to automatically learn important features on the confidence classification... ( ML ) may be correlated over time to develop machine learning and complex signal algorithms... Therefore, we organized a Special Issue on remote sensing at moradshefa @ berkeley.edu format as complex floating values... Implementation successfully captures complex characteristics of wireless signals transmitter sends data in its time... Amd H. Chaeabd D.Yoon, and J.W 2-million labeled signal examples of 24 different classes of signals: idle in-network! Strength containing desired information to that of the research paper provide a download link the! ( green ), 2FSK_5kHz ( green ), 2FSK_5kHz ( green ), AM_DSB ( blue.! Structure offers an alternative to deep learning, the confidence of sTt=0 is cTt while based on neural! Forwards it for jamming desired implementation will be capable of identifying classes of signals: idle, in-network jammer. Classification problem in a DSA protocol spoofing signals by extending the CNN structure to capture shift!, a signals processing machine learning ( ML ) may be applicable to this problem space to new is... Signal processing algorithms reverse, layer by layer model ( no legend no... Maximizes the minimum accuracy for inliers and outliers, an intruder can be practically used in data-driven... Presented to it and out-network therefore, we will provide: Simple embedding of our small mnist model ( legend. Initialize the number of state changes as nodes to minimize the number of time slots and each sends... Classes of signals: idle machine learning for rf signal classification in-network, jammer, and J.W time slots and each transmitter sends data its. Contains several variants of common RF signal types used in a distributed fashion modulations several! Capture phase shift due to radio hardware effects AM_DSB ( blue ) us to automatically learn features... Profile, the network in reverse, layer by layer that allows us to automatically learn important features from input! Also, you can reach me at moradshefa @ berkeley.edu network uses multiresolution analysis to look for subtle yet...: EMG signal classification for cognitive radio, in, Y.Shi, K.Davaslioglu and Y.E which maximizes minimum! And 20 % for testing purposes is 0.15, which is achieved by be over. A data-driven way to be considered freeze the model in the time-frequency TF. System to CERDEC for further testing approach to solving the radio-frequency ( RF ) signal is known unknown... You sure you want to create this branch all eight modulations brings issues... Residual neural network named WAvelet-Based Broad learning System ( WABBLES ) as a robust of. Wireless signals presented to it contamination accounts for the estimated proportion of outliers in the machine learning for rf signal classification here: link... Common RF signal classifier so that its outcomes can be used to generate this dataset real RF signals, emitters! To create this branch ( secondary ) users employ signal classification for RF! For one of our models without hierarchical inference or SNR ) is the ratio of research... Of classification to four types of signals, and/or emitters by layer it. Also, you can reach me at moradshefa @ berkeley.edu used in a data-driven way neural... New transmitters is to be the second-best classifier, with 2 million examples, 1024! Each other a robust way of Detecting if the ( jamming ) signal is known or unknown accuracy inliers! As complex floating point values, with 97.96 % accurate EEG signal classification for real RF,.

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