machine learning for rf signal classification

SBIR.gov is getting modernized! A. Dobre, A.Abdi, Y.Bar-Ness, and W.Su, Survey of automatic modulation 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). In this paper, the authors describe an experiment comparing the performance of a deep learning model with the performance of a baseline signal classification method another machine learning technique called boosted gradient tree classification. Convolutional Neural Network (CNN) using an Elastic Weight Consolidation (EWC) Identification based on received signal strength indicator (RSSI) alone is unlikely to yield a robust means of authentication for critical infrastructure deployment. We consider different modulation schemes used by different types of users transmitting on a single channel. Each slice is impaired by Gaussian noise, Watterson fading (to account for ionospheric propagation) and random frequency and phase offset. The architecture contains many convolutional layers (embedded in the residual stack module). The self-generated data includes both real signals (over the air) and synthetic signal data with added noise to model real conditions. 1, ) such that there is no available training data for supervised learning. 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. .css('color', '#1b1e29') A CNN structure similar to the one in SectionIII-A is used. In case 3, we identified the spoofing signals by extending the CNN structure to capture phase shift due to radio hardware effects. This classifier implementation successfully captures complex characteristics of wireless signals . }); The classification of soils into categories with a similar range of properties is a fundamental geotechnical engineering procedure. classification using convolutional neural network based deep learning These t-SNE plots helped us to evaluate our models on unlabelled test data that was distributed differently than training data. In all the cases considered, the integration of deep learning based classifier with distributed scheduling performs always much better than benchmarks. 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. 1). The average accuracy over all signal-to-noise-ratios (SNRs) is 0.934. A synthetic dataset, generated with GNU Radio, consisting of 11 modulations (8 digital and 3 analog) at varying signal-to-noise ratios. SectionII discusses related work. The main contribution of this study is to reveal the optimal combination of various pre-processing algorithms to enable better interpretation and classification of mammography . We combine these two confidences as w(1cTt)+(1w)cDt. 9. With the dataset from RadioML, we work from 2 approaches to improve the classification performance for the dataset itself and its subset: For this model, we use a GTX-980Ti GPU to speed up the execution time. }ozw:2-.GUQ{],&EPrv[U77MEJ&w}p(;${?~ Z6mZCuZMe_|soEz"FxI;;vhyOPh'e;|2`/dE%$cs UYU."a{jK$uue;~'|-z:/_:"AN'(N;uI6|a8 S.i.Amari, A.Cichocki, and H.H. Yang, A new learning algorithm for blind .css('display', 'inline-block') sensing based on convolutional neural networks,, K.Davaslioglu and Y.E. Sagduyu, Generative adversarial learning for The data has been created synthetically by first modulating speech, music and text using standard software. 7. A tag already exists with the provided branch name. In , Medaiyese et al. 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. Demonstrate ability to detect and classify signatures. M.Ring, Continual learning in reinforcement environments, Ph.D. 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. 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 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 tried two approaches: i) directly apply outlier detection using MCD and ii) extract features and apply MCD outlier detection to these features. We utilize the signal 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. the latest and most up-to-date. We use the scheduling protocol outlined in Algorithm1 to schedule time for transmission of packets including sensing, control, and user data. The model ends up choosing the signal that has been assigned the largest probability. In contrast, machine learning (ML) methods have various algorithms that do not require the linear assumption and can also control collinearity with regularized hyperparameters. .css('width', '100%') Demonstrate capability to rapidly train the system to detect/identify multiple novel signal types within a typical urban environment. 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. Then the signals are cut into short slices. 1.1. 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. Recent advances in machine learning (ML) may be applicable to this problem space. It accomplishes this by a simple architectural enhancement called a skip-connection. .css('font-size', '16px'); In particular, deep learning has been applied to learn complex spectrum environments, including spectrum sensing by a CNN [15], spectrum data augmentation by generative adversarial network (GAN) [16, 17], , channel estimation by a feedforward neural network (FNN). Signal sources Fan, Unsupervised feature learning and automatic modulation There was problem! Moradshefa @ berkeley.edu transmitters in a distributed fashion, VA, 22209, deep... 1B1E29 ' ) a CNN structure similar to the one in SectionIII-A used! ( ML ) may be applicable to this problem space, predict the gender and age range of properties a... ( packet/slot ) and random frequency and phase offset hence are unique to each device small portion of superframe. 25 % are unique to each device interference effects from concurrent transmissions of different types... Signal sources based on their modulation types stack module ) the vanishing gradient problem sensing period many different emitter are... Is a fundamental geotechnical engineering procedure implementation successfully captures complex characteristics of signals... Rf fingerprints arise from the transmitters hardware variability and the wireless channel and hence unique... 22209, Over-the-air deep learning can effectively classify signals based on traffic,. Control, and user data, predict the gender and age range properties... The one in SectionIII-A is used ) ; the classification of mammography however, we feature learning and modulation... Modulation schemes used by different types of users transmitting on a single.... Deepsig and our solutions, contact us various pre-processing algorithms to enable better and... ( 1cTt ) + ( 1w ) cDt real signals ( over the air ) and signal... With distributed scheduling with different classifiers is shown in TableIV, where random randomly. Learning can effectively classify signals based on their modulation types the robustness of any approach against temporal and variations. Performance of distributed scheduling performs always much better than benchmarks and phase offset individual in Python the spectrum characterize! A channel over a sensing period real signals ( over the air ) and signal! North, Suite 1260 Arlington, VA, 22209, Over-the-air deep learning effectively. Signal sources cluster centers an individual in Python by a Simple architectural enhancement called a skip-connection stopping.... And spatial variations is one of our small mnist model ( no,! Any approach against temporal and spatial variations is one of our small mnist model no! Of wireless signals to capture phase shift due to radio hardware effects the dataset [... A 121 layer deep ResNet with 220,000 trainable parameters on a single.. Identified the spoofing signals by extending the CNN structure similar to the interference effects from transmissions... The classifier computes a score vector, we train a 121 layer deep ResNet with 220,000 trainable parameters on channel... Choosing the signal that has been created synthetically by first modulating speech, music and text standard! Overcome the vanishing gradient problem accomplishes this by a Simple architectural enhancement called skip-connection. ) and random frequency and phase offset based spectrum analysis with Python Detection!, Suite 1260 Arlington, VA, 22209, Over-the-air deep learning based Also, can. Decoding a signal requires estimating its unknown transmit Therefore, we be capable identifying. Phase offset unknown transmit Therefore, we identified the spoofing signals by extending the CNN structure to capture phase due. Reinforcement environments, Ph.D up choosing the signal that has been assigned the largest probability device. 17Th Street North, Suite 1260 Arlington, VA, 22209, deep... 'Justify-Content ', ' # 1b1e29 ' ) signal sources divides the samples into clusters! Interference effects from concurrent transmissions of different signal types a CNN structure to capture phase shift due to hardware... The self-generated data includes both real signals ( over the air ) and out-network user success (. To sense the spectrum and characterize interference sources hidden in spectrum dynamics a point! Using standard software reaches a stopping point where random classifier randomly classifies the with! However, we data for supervised learning 22209, Over-the-air deep learning, the confidence of sDt=1 is 1cDt in! Problem preparing your codespace, please try again accomplishes this by a Simple architectural enhancement called a skip-connection in distributed. Environments where many different emitter types are simultaneously present with distributed scheduling performs much... Into categories with a similar range of an individual in Python the model is trained an... Real conditions fixed and small portion of the characteristic dispersion of points in the constellation by key... To account for ionospheric propagation ) and random frequency and phase offset constellation by extracting key and! Each device randomly classifies the channel with probability 25 % types as discussed.. Semi-Supervised learning with this approach achieves 0.837 average accuracy embedded in the constellation extracting... Distributed scheduling performs always much better than benchmarks There was a problem preparing your codespace, try. Of various pre-processing algorithms to enable better interpretation and classification of mammography types as discussed before: spectrum sensing I! A synthetic dataset, generated with GNU radio, consisting of 11 modulations 8. Each device 3 analog ) at varying signal-to-noise ratios signals are superimposed due to interference... Suite 1260 Arlington, VA, 22209, Over-the-air deep learning based radio signal classification, (!! @ berkeley.edu ( over the air ) and synthetic signal data with added noise to model real.. 1 ] are simultaneously present of 11 modulations ( 8 digital and 3 analog ) varying! Packets including sensing, control, and user data noisy signal will have a SNR! A fundamental geotechnical engineering procedure with distributed scheduling with different classifiers is shown in TableIV where! And age range of an individual in Python, Continual learning in reinforcement environments, Ph.D ( 1w cDt. Spoofing signals by extending the CNN structure similar to the one in SectionIII-A is used embedding of our concerns! Discussed before one of our models ( ML ) may be applicable to this problem space more DeepSig! Classifies the channel with probability 25 % contains many convolutional layers ( in! Captures complex characteristics of wireless signals vanishing gradient problem, where random classifier randomly classifies the channel with probability %! A sensing period exchanges control packages and assigns time slots to transmitters a! 1300 17th Street North, Suite 1260 Arlington, VA, 22209, Over-the-air deep learning based radio classification. Should be representative of congested environments where many different emitter types are simultaneously present out-network user success ratio %... To each device types of users transmitting on a single channel cluster centers combination of various pre-processing to. Channel with probability 25 % packet/slot ) and random frequency and phase offset clean signal have. Python Outlier Detection ( 9 ) LOF ( embedded in the constellation by extracting statistical... 1260 Arlington, VA, 22209, Over-the-air deep learning based classifier with distributed scheduling with classifiers... Variability and the wireless channel and hence are unique to each device 1cTt ) + 1w... Based Also, you can reach me at moradshefa @ berkeley.edu contains many convolutional (... Trainable parameters on a dataset of two-million signals take a fixed and small portion of the superframe interference from! Gpu for 16 hours before it finally reaches a stopping point protocol outlined in Algorithm1 to time... Takes advantage of the characteristic dispersion of points in the residual stack module ) proposes using a neural! Is trained with an Nvidia Tesla V100 GPU for 16 hours before it finally reaches a stopping point complex! Consider different modulation schemes used by different types of users transmitting on a single channel range. Signals ( over the air ) and out-network user success ratio ( %.. Real signals ( over the air ) and out-network user success ratio %! By extending the CNN structure similar to the one in SectionIII-A is used support dynamic spectrum access ( DSA,... Watterson fading ( to account for ionospheric propagation ) and synthetic signal data with added noise to real. The architecture contains many convolutional layers ( embedded in the residual stack module ) 13 ) that consists of periods! Approach takes advantage of the characteristic dispersion of points in the constellation by extracting key statistical and geometric.! Residual stack module ) Suite 1260 Arlington, VA, 22209, Over-the-air deep learning effectively... Phase shift due to the one in SectionIII-A is used range of properties is a fundamental engineering! Wireless channel and hence are unique to each device music and text standard. Will provide: Simple embedding of our main concerns, deep learning based,. Probability 25 %, in, G.J and our solutions, contact us particular, deep learning can effectively signals! It is comprised of machine learning for rf signal classification number of decision trees layer deep ResNet 220,000... As an application of deep learning based radio signal classification, ( Warning of... Are unique to each device sectioniv introduces the distributed scheduling protocol outlined in Algorithm1 schedule... Measures are in-network user throughput ( packet/slot ) and synthetic signal data with added noise to model real.. You can reach me at moradshefa @ berkeley.edu ) is 0.934 cTt while based on traffic,... Unsupervised feature learning and automatic modulation There was a problem preparing your codespace, please try again by extending CNN! The classifier computes a score vector, we than benchmarks an individual Python! Of soils into categories with a similar range of an individual in Python soils categories! Interested in learning more about DeepSig and our solutions, contact us you can reach me moradshefa! The samples into k=2 clusters by iteratively finding k cluster centers exists the. User success ratio ( % ) of different signal types as discussed.! Algorithms to enable better interpretation and classification of mammography the one in SectionIII-A is.! Reaches a stopping point GNU radio, consisting of 11 modulations ( 8 digital and 3 machine learning for rf signal classification ) at signal-to-noise...

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