Blind Modulation Identification Using Machine Learning And Deep Learning Algorithms

Author(s)	Mr. Krishna Murthy Goggi, Mr. Mani Kumar Reddy Moilla
Country	India
Abstract	Blind modulation identification is essential in wireless communication to improve spectrum utilization by automatically recognizing the modulation type of received signals. Recent advances in machine learning and deep learning have led to more robust Automated Modulation Classification (AMC) techniques capable of handling channel impairments. Various classifiers such as Decision Tree, Bagging, KNN, and Deep Learning can be used to classify higher-order digital modulation schemes. While Decision Trees offer simplicity and interpretability with an accuracy of around 82%, Deep Learning achieves the highest performance with about 92% accuracy, demonstrating its superior ability to learn complex signal patterns.
Keywords	Automated Modulation Classification, Deep Learning, Machine Learning, Decision Tree, Wireless Signal Processing.
Field	Engineering
Published In	Volume 6, Issue 4, July-August 2024
Published On	2024-08-05
DOI	https://doi.org/10.36948/ijfmr.2024.v06i04.59862

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research