A Super Light Convolutional Neural Network for Automatic Modulation Recognition in Unmanned Aerial Vehicles based 6G Wireless Network

Authors

Keywords:

Automatic Modulation Recognition, Unmanned Aerial Vehicles, Deep Learning, Super Light Convolution Neural Network, Feature Extraction, Computational Modeling

Abstract

Automatic Modulation Recognition (AMR) is a fundamental capability for Unmanned Aerial Vehicle (UAV) communication systems in sixth-generation (6G) wireless networks. It enables UAVs to intelligently identify and track received signals, supporting reliable connectivity under dynamic environments. In practical UAV applications, AMR methods must achieve high recognition accuracy with minimal computational complexity, since UAV platforms operate under strict constraints in storage, memory, and processing power. While recent Deep Learning (DL)-based solutions have advanced AMR performance, most prioritize accuracy at the cost of significantly larger models and higher computational demands. Conversely, lightweight models often lack the accuracy required for real-time deployment, limiting their practical utility. To overcome these limitations, this paper presents a novel Super Light Convolutional Neural Network (SLCNN) for AMR. Unlike conventional models, SLCNN em-
ploys a carefully optimized architecture with fewer convolutional layers, smaller filters, and pooling operations, combined with Gaussian noise and dropout for robust generalization. This design strategy reduces model size and inference time while preserving high accuracy. The proposed SLCNN was evaluated on the HisarMod 2019.1 dataset and validated across RML 2016.10a, 2016.10b, and 2018.01a datasets. Experimental comparisons with Convolutional Long Short-Term Memory Deep Neural Network (CLNN), Long Short-Term Memory, Gated Recurrent Unit, and Residual Network highlight that SLCNN achieves superior results, attaining 98.50% classification accuracy with significantly reduced computational cost. Furthermore, deployment on the NVIDIA Jetson Orin Nano demonstrates real-time suitability, confirming the model’s effectiveness for UAV-based 6G wireless networks.

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Author Biographies

Debbarni Sarkar, National Institute of Technology Meghalaya

Debbarni Sarkar (Student Member, IEEE) received a Ph.D. degree in 2025 from the National Institute of Technology Meghalaya, India. Her research interests include intelligent Reflecting Surfaces, Non-orthogonal multiple access (NOMA), Internet of things (IoT), Hybrid automatic repeat request (HARQ), and machine learning (ML) and deep learning (DL) applications for wireless communication. She is a reviewer for various IEEE, Elsevier, Springer, Transactions/Journals, and conferences.

Samarth Verma, National Institute of Technology Meghalaya

Samarth Verma received a Bachelor of Technology degree in Electronics and Communication Engineering from the National Institute of Technology Meghalaya, a prestigious institution designated as an institute of national importance, in 2024. During his studies, he cultivated deep expertise in various domains of electronics and communication, with a particular emphasis on cutting-edge advancements in wireless communication and networking. His primary research interests are wireless communication systems, Internet of Things (IoT) networks, and the integration of Artificial Intelligence (AI) and Machine Learning (ML) in these fields.

Rupa Kumari, National Institute of Technology Meghalaya

Rupa Kumari completed her Bachelor of Technology degree in Electronics and Communication Engineering from the National Institute of Technology Meghalaya, a premier institution recognized as an institute of national importance, in 2024. Throughout her academic journey, she has developed a profound expertise in various aspects of electronics and communication, with a specific focus on emerging technologies in wireless communication and networking. Her primary research interests are wireless communication systems, Internet of Things (IoT) networks, and the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques.

Yogita Yogita, National Institute of Technology Kurukshetra, India

Yogita (Senior Member, IEEE) received the Ph.D. degree from the Indian Institute of Technology (IIT) Roorkee, India, in 2016. She has served as an Assistant Professor in the Department of Computer Science and Engineering at the National Institute of Technology Meghalaya, India, from Jan. 2017 to Dec. 2022. She is an Assistant Professor in the Department of Computer Science and Engineering, National Institute of Technology Kurukshetra, India. Her research interests include machine learning, deep learning, data mining, and IoT applications for wireless networks and healthcare. She is a reviewer for various IEEE, Elsevier, Springer, and other journals and conferences.

Vipin Pal, National Institute of Technology Delhi

Vipin Pal (Senior Member, IEEE) received the PhD degree from the Malaviya National Institute of Technology Jaipur, India. He is currently working as Assistant Professor in the Department of Computer Science and Engineering, National Institute of Technology Delhi, India. His research interests are IoT, Wireless Sensor Networks, Soft Computing, Data Mining. He is a reviewer of various IEEE, Elsevier, Springer, and other journals and conferences.

Satyendra Singh Yadav, National Institute of Technology Meghalaya

Satyendra Singh Yadav (Senior Member, IEEE), received the Bachelor of Engineering degree in Electronics and Communication Engineering (ECE) from Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV), a state university of Madhya Pradesh, India, in 2012, and successively, the Ph.D. degree from the National Institute of Technology, Rourkela, India, in 2018. Dr. Yadav is the recipient of the prestigious ERASMUS MUNDUS fellowship. He was with the Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento (INESC-ID), Instituto Superior Técnico Lisbon, Portugal, under India-EU ERASMUS MUNDUS NAMASTE PhD mobility project from 2015 to 2016. He served as a full-time faculty member at the Department of ECE, Indian Institute of Information Technology Design and Manufacturing Kurnool (IIITDM Kurnool), India, from July 2018 to June 2019, and at Indian Institute of Information Technology Vadodara (IIIT-Vadodara), India, for a short period of time. Dr. Yadav is an Assistant Professor at the Department of ECE, National Institute of Technology Meghalaya, India. His research interests include wireless communication, IoT networks, intelligent reflecting surfaces (IRS), and machine learning (ML) applications for 5G and beyond wireless systems. He is a reviewer for many IEEE, Elsevier, and Springer Transactions/Journals, as well as conferences. He has also served as a TPC member for many IEEE conferences.

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Published

2025-11-01

How to Cite

Sarkar, D., Verma, S. ., Kumari, R., Yogita, Y., Pal, V., & Yadav, S. S. (2025). A Super Light Convolutional Neural Network for Automatic Modulation Recognition in Unmanned Aerial Vehicles based 6G Wireless Network. IEEE Latin America Transactions, 23(12), 1305–1317. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/9500

Issue

Vol. 23 No. 12 (2025): Ordinary Issue

Section

Electronics