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End-to-end Learning from Spectrum Data: A Deep Learning approach for Wireless Signal Identification in Spectrum Monitoring applications

Merima Kulin, Tarik Kazaz, Ingrid Moerman, Eli de Poorter

arXiv:1712.03987v1cs.NI

TL;DR

Spectrum monitoring needs methods that can handle complex, massive data while reducing reliance on hand-crafted features and multi-stage pipelines. The paper presents a unified end-to-end deep-learning methodology and evaluates signal representations across modulation recognition and interference detection, finding task-dependent performance differences.

  • Problem

    Existing signal-identification work lacks a comprehensive spectrum-monitoring methodology and sufficient guidance for selecting wireless data representations.

  • Method

    The paper develops an end-to-end deep-learning framework using CNN classifiers trained from wireless signal representations for spectrum-monitoring tasks.

  • Results

    Representation choice affects accuracy by task: amplitude/phase improves modulation recognition, while frequency-domain data performs best for interference detection.

  • Takeaways & Limitations

    Selecting the wireless data representation is important for designing accurate classifiers for different spectrum-monitoring applications.

Abstract

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This paper presents end-to-end learning from spectrum data - an umbrella term for new sophisticated wireless signal identification approaches in spectrum monitoring applications based on deep neural networks. End-to-end learning allows to (i) automatically learn features directly from simple wireless signal representations, without requiring design of hand-crafted expert features like higher order cyclic moments, and (ii) train wireless signal classifiers in one end-to-end step which eliminates the need for complex multi-stage machine learning processing pipelines. The purpose of this article is to present the conceptual framework of end-to-end learning for spectrum monitoring and systematically introduce a generic methodology to easily design and implement wireless signal classifiers. Furthermore, we investigate the importance of the choice of wireless data representation to various spectrum monitoring tasks. In particular, two case studies are elaborated (i) modulation recognition and (ii) wireless technology interference detection. For each case study three convolutional neural networks are evaluated for the following wireless signal representations: temporal IQ data, the amplitude/phase representation and the frequency domain representation. From our analysis we prove that the wireless data representation impacts the accuracy depending on the specifics and similarities of the wireless signals that need to be differentiated, with different data representations resulting in accuracy variations of up to 29%. Experimental results show that using the amplitude/phase representation for recognizing modulation formats can lead to performance improvements up to 2% and 12% for medium to high SNR compared to IQ and frequency domain data, respectively. For the task of detecting interference, frequency domain representation outperformed amplitude/phase and IQ data representation up to 20%.

I. INTRODUCTION

The paper frames end-to-end learning as a unified approach for wireless signal identification in spectrum monitoring, addressing spectrum scarcity and data complexity. It introduces a systematic methodology and evaluates how signal representations affect classification tasks.

  • I. INTRODUCTION: Spectrum scarcity, heavy use, and cross-technology interference motivate monitoring systems that can extract information from massive spectrum datasets.The paper identifies technology recognition, modulation recognition, and interference-source detection as important monitoring tasks.
  • I. INTRODUCTION: End-to-end learning treats technology, modulation, interference-source, and channel identification as wireless signal classification problems.The approach connects sensed-signal representations to classifiers in a unified learning pipeline.
  • I. INTRODUCTION: The paper proposes a conceptual framework and methodology covering spectrum-data collection, signal representation, training-data formation, and deep-neural-network training.This addresses the lack of detailed guidance for applying deep learning across spectrum-monitoring use cases.
  • I. INTRODUCTION: Two case studies evaluate modulation recognition and wireless-technology interference detection using multiple wireless data representations.The modulation study considers BPSK, QPSK, 8PSK, 16-QAM, 64-QAM, CPFSK, and GFSK among the listed formats.
  • I. INTRODUCTION: Prior work applies deep learning to specific signal-classification problems but provides limited methodology for reproduction, extension, and representation selection.Related studies also do not focus on spectrum-monitoring scenarios and their underlying training-data models.
  • I. INTRODUCTION: The paper positions its contribution as a comprehensive treatment that spans data transformation, classifier design, and several representations across classification problems.It describes this as a unified framework for spectrum-monitoring wireless signal identification.

II. CHARACTERISTIC USE CASES FOR END-TO-END LEARNING FROM SPECTRUM DATA

End-to-end learning from spectrum data is presented as a unified way to support spectrum sharing, interference management, and regulation. The section connects sensed data and machine-learning classification to operational spectrum-management decisions.

  • II. CHARACTERISTIC USE CASES FOR END-TO-END LEARNING FROM SPECTRUM DATA: End-to-end learning automatically learns features from simple signal representations and trains wireless classifiers in one step.This avoids hand-crafted expert features and complex multi-stage machine-learning pipelines.
  • Detecting spectral opportunities & Spectrum Sharing: Cognitive radio uses spectrum sensing to infer occupancy, identify unoccupied bands, and share them opportunistically with licensed users.The described process combines reports from cognitive users into a spectrum map for availability decisions.
  • Detecting spectral opportunities & Spectrum Sharing: Cognitive IoT devices use CNN-based analysis to identify emitters, detect interference sources, and locate interference-free channels.These outputs support interference mitigation and coexistence with cognitive-radio and legacy technologies.
  • Spectrum management policy and regulation: Spectrum regulation monitors radio-frequency use to prevent harmful interference and promote optimum spectrum use.Potential interference sources include unauthorized emissions, electromagnetic interference, and technically noncompliant devices.
  • Preliminaries: Machine learning learns data-driven statistical models mapping inputs to outputs, with classification used when outputs are discrete or categorical.The paper describes training examples as input-output pairs used to learn the predictor.

2) Learning the model:

The model-learning process estimates an unknown input-output relationship from noisy training data by optimizing parameterized predictors. In wireless signal identification, this makes machine-learning classification suitable for mapping observed signals to signal classes.

  • 2) Learning the model:: The learning algorithm estimates an unknown function f relating inputs X to outputs Y using training data S.The learned function is intended to predict outputs for unseen inputs.
  • 2) Learning the model:: Noise and other discrepancies corrupt observations, preventing direct extraction of the true model parameters.The generic noisy-data model represents the observations as a model output plus additive errors.
  • 2) Learning the model:: The predictor is parameterized by θ, so estimating the model reduces to estimating the parameter vector θ.This defines a parametric model for the predictor f.
  • 2) Learning the model:: Training loss J averages the point-wise loss across the entire training set rather than estimating θ from one example.The parameter vector is selected by minimizing this training loss.
  • 2) Learning the model:: System identification and machine-learning classification techniques are well suited to wireless signal identification problems.System identification estimates parameters of an input-output mapping.

B. Deep Learning

Deep learning replaces hand-designed feature extraction with hierarchical feature learning in convolutional neural networks. CNNs transform grid-like inputs through convolutions and nonlinear layers, then classify outputs using dense and softmax layers trained with a loss function.

  • B. Deep Learning: Feature engineering transforms raw data into representations suitable for prediction, but highlights machine learning’s inability to automatically extract discriminative information.Feature learning addresses this bottleneck by shifting learning toward the features themselves.
  • B. Deep Learning: Deep learning performs feature learning, and this paper focuses on convolutional neural networks.CNNs learn features through nonlinear transformations across nested layers.
  • B. Deep Learning: CNNs accept multidimensional tensors, including one-dimensional time series and two-dimensional images, as visible-layer inputs.Their topology matches grid-like data structures.
  • B. Deep Learning: A convolution computes local weighted combinations, after which a bias and point-wise nonlinearity produce a feature map.The filters are determined by weights W_l, while b_l is the bias and g is the activation function.
  • B. Deep Learning: The final CNN layers use dense connections and a softmax classifier to compute posterior probabilities over K classes.Training minimizes a loss comparing estimated probabilities with one-hot true labels, commonly using mean squared error or categorical cross-entropy.
  • B. Deep Learning: Regularization combined with dropout is used to control over-fitting by randomly dropping activations during training.The retention probability p is typically selected using validation data or set to 0.5.

C. Deep Learning from spectrum data

End-to-end spectrum learning combines sensing, representation, classification, and decision processes for intelligent wireless-environment analysis. The framework uses raw complex signal data, transforms it into representations, and applies deep learning to identify signals and support spectrum decisions.

  • C. Deep Learning from spectrum data: Figure 2’s end-to-end pipeline connects spectrum sensing, data processing, classification, and decisions for intelligent wireless behavior.The pipeline is presented for future wireless networks operating in dynamic spectrum environments.
  • C. Deep Learning from spectrum data: Data acquisition collects raw samples from multiple spectrum bands and organizes them into vectors r_k representing the received signal’s complex envelope.These vectors are inputs for models that reason about wireless-signal presence.
  • C. Deep Learning from spectrum data: Pre-processing transforms acquired vectors into simple representations such as frequency, amplitude, phase, or spectrum, or into more complex features.Signal-processing tools analyze and manipulate the raw data before classification.
  • C. Deep Learning from spectrum data: Classification identifies emitters, modulation formats, wireless technologies, interference types, or available spectrum bands.The paper refers to this environmental inference process as spectrum learning.
  • C. Deep Learning from spectrum data: Predicted signal information feeds spectrum decisions such as selecting transmission bands or power, applying back-off, or enforcing spectrum policies.These decisions are described for cognitive radio, cognitive IoT, and spectrum-regulation scenarios.
  • C. Deep Learning from spectrum data: The framework introduces data models for signal representation, feature extraction, training-data creation, and deep-neural-network classification.Deep learning is used to extract low- and high-level features and classify wireless signals.
  • C. Deep Learning from spectrum data: Wireless transmission and reception are affected by carrier-frequency offset, phase noise, timing drift, thermal noise, channel fading, and hardware imperfections.The received signal is therefore a corrupted version of the transmitted signal.

B. Data acquisition

Wireless signal data acquisition converts sensed continuous-time radio signals into segmented vectors of complex samples. These vectors retain information used to identify modulation, wireless technology, or interference sources.

  • B. Data acquisition: Adequate training data must be collected to derive a machine-learning model for wireless signal identification.The data-acquisition process collects wireless signal features for model development.
  • B. Data acquisition: The receiver amplifies, mixes, low-pass filters, and digitizes r(t) at sampling rate f_s = 1/T_s.The resulting discrete signal r_n contains in-phase and quadrature components.
  • B. Data acquisition: Sampling a period T produces N complex raw samples r[n] for n = 0, ..., N − 1.These samples form a time series of complex raw observations.
  • B. Data acquisition: The complex samples are arranged into data vectors r_k representing windowed or segmented portions of the received continuous stream.The segmentation is analogous to windowing in audio signal processing.
  • B. Data acquisition: Each data vector carries information for assessing modulation type, wireless technology, interferer, or another wireless-signal class.The vectors serve as inputs to wireless signal identification models.

C. Wireless signal representation

The paper compares three fixed-length wireless signal representations—IQ, amplitude/phase, and FFT—for extracting discriminative features from spectrum data. Visual examples show that channel and synchronization impairments leave patterns usable by deep learning.

  • Signal representations: Three representations map each complex signal snapshot into a fixed-length real-valued vector: IQ, amplitude/phase, or FFT components.The representations are designed with the same dimension and type in R2xN for unified processing.
  • Transformation 1 (IQ vector): The IQ vector separates raw complex samples into in-phase and quadrature components.
  • Transformation 2 (A/φ vector): The amplitude/phase vector separates each raw complex sample into magnitude A and phase φ vectors.
  • Transformation 3 (FFT vector): The FFT vector represents the signal in frequency space using the real and imaginary components of its complex FFT.
  • Representation examples: Figures visualize IQ time plots, amplitude and phase time plots, and frequency magnitude spectra for modulation examples.The examples use 128 samples from the RadioML Modulation dataset and include channel and synchronization impairments.
  • Representation comparison: The study tests how representation choice influences classification accuracy while training models on the resulting feature vectors.One model uses raw samples, one uses amplitude and phase, and one uses frequency-domain data for feature extraction.

D. Wireless signal classification

Wireless signal identification is formulated as a supervised K-class classification problem using transformed spectrum vectors and one-hot signal labels. After training, the classifier outputs class probabilities and selects the highest-scoring class.

  • Problem formulation: Identifying one signal from K possible wireless signal types is formulated as a K-class machine-learning classification problem.
  • Training data: Each measured data vector is transformed into a feature vector, stacked into an observation matrix, and paired with a one-hot encoded signal label.
  • Model estimation: The resulting feature-label pairs are used to estimate the parameters θ of the wireless signal classifier f.
  • Inference: Training requires prior knowledge of the transmitted signal type, but the trained model can identify signals automatically without that information.
  • Inference: For each input, the predictor estimates P(y_i = k|x_i; θ) and chooses the class with the highest score.

V. EVALUATION SETUP

The evaluation examines end-to-end CNN learning for modulation recognition and wireless interference identification using three alternative signal representations. The setup creates separate datasets for IQ, amplitude/phase, and FFT inputs.

  • Evaluation tasks: Two use cases are evaluated: radio signal modulation recognition and wireless interference identification.
  • Radio modulation recognition: Modulation recognition identifies the modulation structure of received signals and supports understanding the communication scheme and emitter.The paper describes it as important for spectrum regulation and dynamic spectrum access.
  • Wireless interference identification: Wireless interference identification determines the type of coexisting emitter operating in the same frequency band.The paper relates this task to interference mitigation and coexistence management in unlicensed bands such as the 2.4GHz ISM band.
  • Data representations: For each task, CNNs are trained on IQ, amplitude/phase, and FFT representations, producing one dataset per transformation.
  • Radio modulation recognition: The modulation experiment uses 11 labeled formats from RadioML 2016.10a, sampled in N = 128 batches and corrupted by noise, timing, phase, and channel distortions.The data vectors contain 8 to 16 symbols, and the task is formulated as an 11-class problem.

2) Wireless Interference identification in ISM bands:

The interference-identification study applies CNNs to alternative spectrum representations and evaluates predictions with classification accuracy and class-level precision, recall, and F1 metrics. The reported setup uses a CNN architecture with a 15-class interference output.

  • CNN network structure: The CNN architecture receives unified 2x128 IQ, FFT, or amplitude/phase inputs and estimates the likelihood of each interference class.Its softmax output uses a 15-dimensional one-hot class representation for wireless interference identification.
  • CNN network structure: The network uses two convolutional layers, dropout regularization with p = 0.6, and a 256-neuron fully connected ReLU layer before softmax classification.
  • Training and validation: The CNNs are trained and validated using a 67% training split and 33% testing and validation split.
  • Training and validation: The interference-identification CNNs require approximately 60 seconds per epoch on the stated GPU platform.
  • Evaluation metrics: Overall classification accuracy is computed as 1 − E_test after defining test error over m_test testing snapshots.
  • Evaluation metrics: Per-class precision, recall, and F1 are combined using prevalence-weighted macro-averages, with confusion matrices providing detailed class-level performance.

E. Numerical results

The CNN classifiers perform better for interference identification than modulation recognition, while representation choice affects which signals are easiest to discriminate. Modulation errors reflect signal similarities and channel distortions, with amplitude/phase information improving some class separations.

  • Overall performance: Interference classifiers achieve Pavg, Ravg and F1avg values of 0.98–0.99 at high SNR, 0.94–0.99 at medium SNR, and 0.81–0.90 at low SNR.Modulation classifiers are less robust across SNR scenarios and generally achieve lower performance.
  • Overall performance: Under low SNR, modulation-recognition classifiers achieve metrics between 0.22 and 0.36.The modulation-recognition models degrade substantially more than the interference classifiers in this scenario.
  • Interpretation: Modulation-recognition performance is affected by a time-varying multipath fading channel and transceiver impairments, whereas interference identification uses a simpler flat-fading channel.The differing channel models and signal-discrimination tasks may help explain the performance gap between the two case studies.
  • Modulation recognition: At SNR 6dB, all representations classify AM-DSB, AM-SSB, BPSK, CPFSK, GFSK and PAM4 with high accuracy.The main modulation-recognition confusion is QAM16 misclassified as QAM64 because QAM16 is a subset of QAM64.

2) Noise Sensitivity:

Noise sensitivity differs across the two tasks and data representations. Amplitude/phase is advantageous for modulation recognition at higher SNR, whereas FFT data is strongest for interference identification, especially at low SNR.

  • Evaluation setup: Testing uses the same sets labelled from −20dB to +20dB to estimate classifier sensitivity to noise and the communication range of effective use.Figures 8 and 9 report the modulation-recognition and interference-identification results, respectively.
  • Modulation recognition: For modulation recognition, the three CNN models perform similarly below −10dB, while amplitude/phase outperforms frequency-domain data by up to 2% and 12% at high SNR.The cited comparison reports accuracy improvements for the amplitude/phase model over the IQ and frequency-domain models, respectively.
  • Modulation recognition: For modulation recognition, frequency-spectrum data does not improve accuracy over IQ data and is sub-optimal when classes share frequency-domain characteristics after distortion.QPSK, 8PSK, QAM16 and QAM64 are identified as particularly similar in the frequency domain.
  • Interference identification: For interference identification, FFT data performs best across SNR scenarios and offers at least ∼4dB performance gain and ∼9% classification-accuracy improvement over IQ data at low SNR.ISM-band technologies have more expressive frequency-domain features, including differing bandwidth and modulation or spreading methods.
  • Interference identification: The interference-identification advantage of FFT data is attributed to distinguishable frequency-spectrum characteristics among ZigBee, WiFi and Bluetooth.These characteristics include bandwidth and modulation/spreading method.

3) Takeaways:

The paper presents end-to-end learning as a unified approach for spectrum monitoring, while showing that representation choice must be matched to the signal-discrimination task. Its broader deployment remains constrained by large-scale sensing and data-management challenges.

  • Takeaways: End-to-end learning can detect radio emitters across varied wireless-signal tasks without designing expert features, using CNNs to learn signal features and classifiers jointly.The paper frames technology, modulation, interference and channel identification as wireless signal identification problems.
  • Takeaways: Classifier performance depends on data representation and on the similarities among the wireless signals being differentiated.Signals that look similar in one representation are harder for the model to discriminate.
  • Open challenges: The deep-learning framework for spectrum-utilization optimization is still in its infancy.The paper presents this area as an emerging interdisciplinary field with important unresolved challenges.
  • Open challenges: A practical boundary is that cognitive spectrum monitoring requires scalable infrastructure for distributed sensing, transfer and storage across wide frequency ranges.Heterogeneous technologies and multiple monitored bands produce spectrum data volumes and velocities far above typical wireless-sensor reports.
  • Methodology: The proposed methodology covers spectrum-data collection, representation design, training-data formation and deep-neural-network training for wireless signal classification.The paper validates the methodology on modulation recognition and wireless interference identification.
  • Takeaways: For interference detection, FFT-trained models outperform amplitude/phase and IQ models by up to 20% at low SNR and up to 5% at medium-high SNR.The result supports evaluating multiple representations for each spectrum-monitoring task.
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