Article
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Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning
Version 1
: Received: 24 August 2023 / Approved: 25 August 2023 / Online: 25 August 2023 (07:26:05 CEST)
A peer-reviewed article of this Preprint also exists.
Dgani, B.; Cohen, I. Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning. Sensors 2024, 24, 701. Dgani, B.; Cohen, I. Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning. Sensors 2024, 24, 701.
Abstract
This paper presents a novel method for automatic modulation classification (AMC) for cognitive radio (CR) networks based on a simple classifier that is trained with high-order cumulant. The proposed method focuses on the statistical behavior of modulated signals and includes analog modulation and digital schemes, which received less attention in the literature. The effectiveness of the proposed method is demonstrated through simulation results using high-quality generated signals under different signal-to-noise ratios (SNRs) and channel conditions. The classification performance achieved by the proposed method is superior to that of the more complex deep learning methods, making it well-suited for deployment in end units of CR networks, particularly in military and emergency service applications. The proposed method offers a cost-effective, high-quality solution for AMC that meets the stringent requirements of these critical applications.
Keywords
Cumulants; Modulation Classification; Machine Learning.
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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