Automatic Modulation Identification in Receiver Environment using Machine Learning Techniques

Author(s)	Mr. MANI KUMAR REDDY MOILLA, Mr. NARASIMHA REDDY BUCHUPALLI, Mr. RAVI SANKAR KOTHURU, Mr. KRISHNA MURTHY GOGGI
Country	India
Abstract	Security is vital in many communication systems, especially in military applications. Adaptive Radio Systems (ARS) use adaptive modulation to provide secure and efficient transmission. For Cognitive Radio (CR), intelligent receivers must identify the modulation of incoming signals automatically. Automatic Modulation Identification (AMI) is therefore a key function. In this work, four classifiers—Decision Tree, Support Vector Machine, k-Nearest Neighbor, and Random Forest—are evaluated and compared to determine the most effective method for accurate modulation classification.
Keywords	: Automatic Modulation Identification Support vector machine, k- Nearest neighbor, Decision Tree, Rain forest, Cognitive Radi, Adaptive radio system
Field	Engineering
Published In	Volume 6, Issue 5, September-October 2024
Published On	2024-09-13
DOI	https://doi.org/10.36948/ijfmr.2024.v06i05.59999

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