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Radio Signal Classification with Deep Neural Networks
- 1. Radio Signal Classification with Deep Neural Networks 2018 Army Signal Classification Challenge 6th place solution Kachi Odoemene 29 Aug 2018
- 2. Competition • Army Rapid Capabilities Office (RCO) – April 30th – August 13th 2018 – $150,000 in prizes • Radio signal modulation classification – Automatically identify modulation type of received radio signal – 24 modulation classes, including noise class
- 3. Competition • Finished 6th place (of 161 participants)
- 4. Radio Signal Modulation Schemes Binary Frequency Shift Keying (BFSK)
- 5. Quadrature Signals • I/Q: In-phase and Quadrature components • 90° shift between identical periodic signals – e.g. sine and cosine wave Q I
- 6. I/Q Modulation • Summation of I/Q pairs results in any modulation (frequency, amplitude, phase, etc) • Transmitted & received radio signal represented as I/Q components Modulated RF Signal I channel, I(t) Q channel, Q(t) Inputs (Modulating waves) Carrier wave
- 7. I/Q Time Domain Examples – high SNR PSK: phase shift keying FSK: frequency shift keying ASK: amplitude shift keying MSK: Minimum (frequency) shift keying QAM: quadrature amplitude modulation CP: continuous phase OQ: offset quadrature SNR: Signal to Noise
- 8. I/Q Time Domain Examples – low SNR
- 9. Dataset • > 30 GB – 4.32 million I/Q instances • Dimension: 2 x 1024 – Short time window, real world conditions – 24 modulation classes – White noise added to signals • 6 signal-to-noise (SNR) levels • Test datasets (public) – 2 sets of 100000 I/Q instances (unlabeled)
- 10. Evaluation Metric • Derived from multiclass log loss Score =
- 11. Goal • Automatically identify the modulation type of the received radio signal – What modulation format was employed? – Eg. Military applications: • Spectrum surveillance, electronic warfare, and threat analysis – Identify modulation type of intercepted enemy communication
- 12. Traditional approach • Hand-crafted features (feature engineering) – Higher order statistics – Autocorrelation and spectral correlation functions – Measures derived from instantaneous frequency, amplitude, phase • Mean, standard deviation, kurtosis, etc – Small number of features (28-32) • SVM, decision trees, ensemble, neural networks
- 13. Deep Neural Networks for Radio Modulation Recognition (2016) Convolutional networks outperform expert feature-based classifiers
- 14. • Proposed 3 additional architectures for modulation recognition: – Inception – ResNet – Hybrid of Convolutional, Long short term memory (LSTM), and Fully Connected (FC) Deep Neural Network (CLDNN) • No source code provided • Sparse details on architecture hyperparameters (2017)
- 15. Residual Unit Layer Input Model 1: ResNet (original) • Image classification • CNNs with skip (residual or shortcut) connections – Feed previous representations (activations) into downstream layers – Prevents information loss • Enables training of deeper networks – 100s to 1000s of layers He et al Deep Residual Learning for Image Recognition (ArXiv 2015) Skipconnection
- 16. Input (2x1024) Conv (1x128) D-Conv (2x1) Avg. Pool “ResBlock” x4 Global Avg Pool FC (128) x2 Softmax (24) (Temporal) (Spatial) Model 1: ResNet (modified) • Temporal convolution on each IQ channel separately • Depth-wise convolution (D- Conv) • Batch normalization after convolutional and fully connected (FC) layers • Multiple residual units within ResBlock • # Parameters: 255,944 He et al Deep Residual Learning for Image Recognition (ArXiv 2015)
- 17. Model 2: CLDNN (original) • Speech recognition • Unified model: CNN, LSTM, FC • CNN: reduce spectral variations of input data • LSTM: learn temporal structure • FC: transform LSTM features into output easy to classify Sainath et al Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks (IEEE, 2015) Convolutional layers Linear layer LSTM layers Fully Connected layers Output targets (1) (2) C C L L D D Dim. red.Xt [Xt-l,..,Xt,…,Xt+r]
- 18. Model 2: CLDNN (modified) • Temporal convolution on each IQ channel separately • Depth-wise convolution (D-Conv) • Batch normalization after each convolutional and fully connected (FC) layers • “ConvBlock”: Conv + BatchNorm + ReLU • Dropout between FC layers • # Parameters: 147,480 Sainath et al Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks (IEEE, 2015) Input (2x1024) Conv (1x128) D-Conv (2x1) Avg. Pool ConvBlock x2 Concatenate LSTM (48) x2FC (128) Softmax (24) (Temporal) (Spatial)
- 19. Data preparation • Raw data, no preprocessing • Trained from scratch • Data split – Train: 80%, Valid: 13.33%, Holdout: 6.67%
- 20. Implementation details • Hyper-parameter selection – Temporal filter kernel size – Number of FC units • Maximum of 25 epochs, early stopping • Adam optimizer • Learning rate: 1e-3 • Keras (Tensorflow) • Hardware (Personal) – GTX 1080Ti (11 GB) GPU – 16 GB RAM
- 21. Loss curves
- 22. Accuracy per modulation class: all SNRs
- 23. Accuracy per modulation class: high SNR
- 24. Accuracy per SNR: pooled across classes
- 25. Accuracy per SNR: individual classes PSK: phase shift keying FSK: frequency shift keying ASK: amplitude shift keying MSK: Minimum (frequency) shift keying QAM: quadrature amplitude modulation CP: continuous phase OQ: offset quadrature
- 26. Leaderboard performance Model TestSet1 Score TestSet2 Score Final Score CLDNN 42.09 54.22 50.58 ResNet-18 33.27 58.27 50.77 Combined 42.09 58.27 53.41 Final Score = 0.3*TestSet1 + 0.7* TestSet2
- 27. Ensembling
- 28. Ensembling • Trained meta-learner to combine predictions from multiple models
- 29. Leaderboard performance: Ensembling Model TestSet1 Score TestSet2 Score Final Score CLDNN 42.09 54.22 50.58 ResNet-18 33.27 58.27 50.77 Combined 42.09 58.27 53.41 Logistic Regression 45.17 63.25 57.83 Light GBM 46.40 64.54 59.10 Final Score = 0.3*TestSet1 + 0.7* TestSet2
- 30. Challenges • Structural (competition organization) – Multiple changes to scoring procedure and test set, submission site shutdown, etc • Time constraint – 1 submission/day (10am) & leaderboard update (5pm) – On Kaggle: up to 5 submissions/day, immediate LB update • Technical – Hardware failure final week of competition • Backup: Google Colab
- 31. Future Efforts • Incorporate additional features: – Amplitude – Phase difference – Magnitude of Fourier transform – Spectrogram • Explore other architectures & ensembling methods – Inception-like architecture: process and combine multiple frequency scales • Model interpretability – DeepLIFT (Deep Learning Important FeaTures) – LIME (Local Interpretable Model-agnostic Explanations) – Ablation studies
- 32. Acknowledgements
Notes de l'éditeur
- Radio Signal Modulation Recognition Radio Signal Classification with Neural Networks
- Blind signal classification- little to no prior knowledge of signal being detected
- Waves consists of phase, amplitude, and frequeny Input signal is the data/information you wish to transmit. This information is mixed with a carrier wave. Analog modulation: input signal varies continuously Digital modulation: input signal are discrete values of 1’s and 0’s. analog data such as speech (sampled at some rate) is first compressed and then converted into bit stream (1s and 0s). This is used as the input signal. Also include phase modulation and combinations of all three Image from https://www.taitradioacademy.com/topic/how-does-modulation-work-1-1/
- Radio signal represented as I/Q component Modulating inputs are the data to be sent RF carrier waves are in quadrature, 90deg shift ----- Meeting Notes (8/28/18 04:45) ----- Right down equation of received signal from OShea paper Explain that this is transmitted signal
- Panel of images (n=24) Received I/Q samples sampled from a desired carrier frequency
- Panel of images (n=24)
- Hardware (Personal) GTX 1080Ti (11 GB) GPU 16 GB RAM
- Minimize logloss Probability assigned to each class Heavily penalizes confident(high probability) and wrong predictions more than it rewards confident (high probability) and correct predictions the sum of log loss values for each class prediction in the observation Nuanced view of model performance, takes into account uncertainty of predictions.
- Traditional approach to automatic modulation classification Expensive and tedious to develop analytically, also not flexible Digital modulations e.g. HOS (moments and cumulants) and Analog modulations for measures derived from instantaneous phase, amplitude, frequency, General example of expert feature: compute the m-th order statistic on n-th power of the instantaneous or time delayed received signal max value of spectral power density of normalized instantaneous amplitude, standard deviation of absolute value of nonlinear component of instantaneous phase of non ----- Meeting Notes (8/28/18 04:45) ----- extract 28-32 features
- Fortunately the hard work of figuring out whether neural networks can be successfully applied to the problem of modulation recognition had been worked out by Tim O’Shea and colleagues Harder these days to find a problem where deep learning has not already been applied Learn features from the data directly ----- Meeting Notes (8/28/18 18:35) ----- Replace with deep neural networks
- Proposal based on well performing architectures in computer vision Like most papers posted on Arxiv, the paper did not provide source code for the models and was very light on details, hence I had to reverse engineer and run test experiments Settled on two models Done with 8 to 11 modulation classes, not clear whether it would scale to 24 modulations
- I made modifications to original ResNet architecture, namely adding the temporal convolution Skip connection or shortcut, which feed activations from one layer into the next layer Allows training of deep networks Plain networks without skip connections tend to struggle when layers are too deep, unable to choose parameters, leading to overfitting. Residual networks on the other hand, learn the identity function because of the skip connections, which leads to better training ----- Meeting Notes (8/28/18 18:35) ----- fix skip connection
- Parameters less than the number of samples, helps to regularize the model
- CLDNN model first developed by Google for speech recognition applications leverage advantages of convolution and rnn layer CNNs to learn spectral features and reduce spectral variations in the input LSTMs to learn temporal variations FC to transform LSTM features into easily classifiable format LSTMs on their own need better input features, and easily classifiable outputs, hence need help from
- CLDNN model first developed by Google for speech recognition applications leverage advantages of convolution and rnn layer One LSTM layer
- Hold out set for local evaluation ----- Meeting Notes (8/29/18 05:41) ----- no augmentation
- ----- Meeting Notes (8/28/18 04:45) ----- complete learning rate statement ----- Meeting Notes (8/28/18 18:35) ----- Skip!
- ----- Meeting Notes (8/28/18 18:35) ----- Fix this figure legend!!
- ----- Meeting Notes (8/28/18 04:45) ----- Pooled across SNRs
- Might have trouble with high and low frequency
- Suggests CLDNN might generalize better to unseen classes
- If you’ve ever participated in a machine learning competition on platforms like Kaggle, you know that ensembling predictions from multiple models
- ----- Meeting Notes (8/28/18 04:45) ----- calculate percentages final submission bold
- Multiple changes to judging criteria and testset Submission site failure 1 submission per day, compared to multiple (up to 5 on Kaggle) ----- Meeting Notes (8/28/18 18:35) ----- update 5pm
- Things that I didn’t get to try Magnitude of the square of the ----- Meeting Notes (8/28/18 04:45) ----- Magnitude of the frequency spectrum ----- Meeting Notes (8/28/18 18:35) ----- add ablation!
- for organizing and hosting the Army Signal Classification Challenge
- Powerful application to real world problems Moving beyond classifiers