Automatic Modulation Recognition using ResNet Architecture

Year 2026, Volume: 9 Issue: 1, 16 - 24, 15.01.2026

Bedirhan Şendur , Mümtaz Yılmaz

https://doi.org/10.34248/bsengineering.1783964

https://izlik.org/JA92WA78KX

Abstract

This work focuses on the application of deep learning models for the classification and identification of signals modulated using both analog and digital modulation techniques. The paper aims to explore the potential of advanced neural networks in accurately distinguishing between various modulation schemes, which play a critical role in modern communication systems. By leveraging large datasets and employing robust machine learning frameworks, the study evaluates the performance of these models in terms of accuracy, efficiency, and reliability under different signal-to-noise ratio (SNR) conditions. Furthermore, the paper provides a comparative analysis of the models, highlighting their strengths and limitations in handling diverse modulation types. The results of this work contribute to the development of intelligent communication systems capable of adapting to dynamic environments, ensuring robust and efficient signal processing.

Keywords

Deep learning , ResNet , Modulation , Classification

Ethical Statement

Ethics committee approval was not required for this study because there was no study on animals or humans.

Project Number

N/A

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