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Related Concept Videos

Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Basic Discrete Time Signals01:16

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Receiver Operating Characteristic Plot01:15

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Related Experiment Videos

Deep Learning-Based Automatic Modulation Classification for OFDM Signals: From Synthetic Training to OTA Evaluation.

Raluca Nelega1,2, Mate-Marton Mezei2, Zsolt Alfred Polgar2

  • 1National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, Romania.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

Large-scale synthetic datasets improve Automatic Modulation Classification (AMC) for Cognitive Radio (CR) systems. This research shows that training Convolutional Neural Networks (CNNs) on synchronized, synthetic data enhances generalization to real-world conditions.

Keywords:
CNNautomatic modulation classificationcross-domain generalizationdeep learningsignal processing

Related Experiment Videos

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Radio frequency (RF) spectrum congestion necessitates efficient spectrum utilization.
  • Cognitive Radio (CR) systems use Automatic Modulation Classification (AMC) for dynamic spectrum access.
  • Accurate AMC in Orthogonal Frequency Division Multiplexing (OFDM) is challenging due to dynamic channel conditions.

Purpose of the Study:

  • To evaluate the impact of dataset scale, synthetic impairments, and hardware impairments on the cross-domain generalization of Convolutional Neural Network (CNN) architectures for OFDM AMC.
  • To assess the performance of CNN-based AMC models trained on diverse datasets when evaluated on unseen Over-The-Air (OTA) data.
  • To identify optimal data generation strategies for robust AMC model generalization.

Main Methods:

  • Utilized a Convolutional Neural Network (CNN) architecture for Automatic Modulation Classification (AMC).
  • Employed 2D amplitude-phase histograms for signal representation.
  • Trained and evaluated CNN models on five distinct datasets: varying scales of synthetic signals with synchronization impairments, and a conducted hardware dataset.
  • Assessed cross-domain generalization by testing on an unseen indoor Over-The-Air (OTA) dataset from 13 positions.

Main Results:

  • The large-scale, synchronization-impaired synthetic dataset demonstrated the best cross-domain generalization performance.
  • The best-performing model achieved a mean indoor OTA accuracy of 93.36%.
  • Training on large-scale synthetic data significantly outperformed training on a limited-size conducted hardware dataset.

Conclusions:

  • Dataset scale and the inclusion of synchronization impairments are critical for achieving robust cross-domain generalization in CNN-based AMC.
  • Effective data generation strategies are essential for developing reliable AMC systems for Cognitive Radio.
  • This study provides a strong baseline for future research in generalized AMC for dynamic spectrum access.