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November 8, 2025International Journal For Multidisciplinary Research

Automatic Modulation Identification in Receiver Environment using Machine Learning Techniques

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Authors

MMMANI KUMAR MOILLANBNARASIMHA BUCHUPALLIRKRavi Teja KothuruUniversity of San Diego

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Overview

Evaluation of classifiers improved modulation classification in Cognitive Radio, suggesting enhanced secure transmission techniques.

Key Points

  • Modulation classification accuracy improved using machine learning techniques in Cognitive Radio systems, enhancing communication security.
  • Support Vector Machine achieved the best performance in identifying modulation with 95% accuracy compared to other classifiers.
  • Assessment involved four machine learning classifiers: Decision Tree, Support Vector Machine, k-Nearest Neighbor, and Random Forest.
  • Evaluation focuses on enhancing secure transmission methods in military communication applications through effective modulation identification.

Cite This Study

MOILLA et al. (2024) studied this question.

synapsesocial.com/papers/690e8b75a5b062d7a4e739fchttps://doi.org/10.36948/ijfmr.2024.v06i05.59999
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Blind Modulation Identification Using Machine Learning And Deep Learning Algorithms2024
  2. 2Modified receiver architecture in software-defined radio for real-time modulation classification2024
  3. 3Automatic Modulation Recognition Method Basedon Multimodal I/Q-FRFT Fusion2024 · 1 citations
  4. 4Deep Learning for Automatic Modulation Classification: A Review2026
  5. 5Research on communication signal modulation style recognition method based on multi-input deep learning2025