Title: Spectrum sharing using deep learning: multi-agent reinforcement learning

Authors: B.V. Santhosh Krishna; A. Bharathidhasan; N. Ashokkumar; K. Periyar Selvam

Addresses: Computer Science and Engineering, Bangalore Technological Institute, Bengaluru, India ' Department of Artificial Intelligence and Data Science (AI&DS), V.S.B. Engineering College, NH-67, Covai Road, Karudayampalayam Post, Karur – 639111, Tamil Nadu, India ' Department of Electronics and Communication Engineering, Mohan Babu University, Sree Sainath Nagar, Sree Vidyanikethan, Tirupati, Andhra Pradesh 517102, India ' Department of Electronics and Communication Engineering, GRT Institute of Engineering and Technology, 5JVX+C4P, Block – A Tirupathi Highway Mahalakshmi Nagar Tiruvallur Dist., Srinivasapuram, Tamil Nadu 631209, India

Abstract: The number of people using cell phones and the requirement for the radio band has increased over the last few years. The fast rise of 5G networks for wireless communication and wireless communication has met this need. There is reason to believe that the issue of improper use of the wireless spectrum could be resolved with the progress of cognitive radio and its spectrum-sensing technology. Deep learning technology is known for being able to learn and change amazingly quickly. The purpose of this research is to provide a brief summary of the approach used in cognitive radio spectrum-sensing technology and deep learning technology. The first part of this study talks about the common spectrum-sensing methods to give a big picture of the benefits of deep learning-based spectrum-sensing algorithms. We find that our method can increase the accuracy of previous work and conventional learning strategies by as much as 83%.

Keywords: cognitive radio; spectrum sensing; wireless communication; cooperative spectrum sensing.

DOI: 10.1504/IJESMS.2026.150576

International Journal of Engineering Systems Modelling and Simulation, 2026 Vol.17 No.1, pp.1 - 8

Received: 20 Apr 2024
Accepted: 17 Mar 2025
Published online: 17 Dec 2025 *

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