Deep Learning based categorization of modulation methods for wireless communications

Registration ID: IJNRD_192023

Published ID: IJNRD2304398

DOI: http://doi.one/10.1729/Journal.33824

Vargil Vijay E , Venkata Ramireddy P , Jubeda M , Padmaja K , Venkateswarlu N

Deep Learning based categorization of modulation methods for wireless communications

Deep learning, a novel approach which is a subset of machine learning, has demonstrated exceptional performance in the processing of images, voices, and natural language. Researchers haven't yet fully analyzed how DL can be used for wireless transmission, though. Recently, it has become more common to use DL technology for wireless communication uses. This article's suitability of a Deep learning-based strategy for classification of modulation methods is discussed. Applications for modulation method classification (MMC) are both private and military. This article proposes a deep learning-based architecture for modulation method classification which is known as Convolutional Neural Network (CNN). In our suggested architecture, we will use a Gaussian noise layer following the convolution layers, which shows a remedial impact during training and lowers the over fitting issue. We want to show that the suggested architecture for modulation classification methods works better than the current machine learning-based architecture.

Published Paper   E-Certificate

Vargil Vijay E , Venkata Ramireddy P, Jubeda M, Padmaja K, & Venkateswarlu N (April-2023). Deep Learning based categorization of modulation methods for wireless communications. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), d712-d719. http://doi.one/10.1729/Journal.33824

Volume 8 Issue 4, April-2023

Pages : d712-d719

Paper Reg. ID: IJNRD_192023

Published Paper Id: IJNRD2304398

Downloads: 000121981

Research Area: Engineering

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Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2304398

Crossref DOI: http://doi.one/10.1729/Journal.33824

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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