An Improved AI-Based Low Latency Data Transmission in 5G Communication Systems
subject: 5G; Artificial intelligence; Communication systems; Data processing; Data transmission; Network performance
description: This paper devised an advanced artificial intelligence (AI) solution for ultra-low latency data transmission in 5G networks. With increasing data rates and lower latency required in 5G networks, efficient methods for transmitting the maximum amount of data are necessary. We have developed an approach that uses AI algorithms so that data transmission can be done more optimally and help reduce latency, providing better overall performance. Our approach consists of several steps, in which we predict the traffic patterns using machine learning techniques in step 1 and allocate network resources accordingly. That helps reduce network congestion and speeds up data transmission. We also introduce deep learning algorithms to adjust the transmission parameters according to network conditions, reducing latency. We simulate our algorithm in 5G network scenarios to assess its performance. The comparison of the results shows that a very low latency was achieved for this design over the earlier methods. Our developed AI-based improved solution provides a potential key to low latency data transmission in 5G communication systems. Integrating AI methods makes the system not only perform better but also be able to adapt more easily when network conditions change. The next steps are to explore the improvements of algorithms and implement them practically in 5G networks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
creator: Gupta, Rishi; Logeshwaran, J.; Dhanasekaran, S.; Kumar, Jitendra; Garg, Amit
source: Lecture Notes in Networks and Systems;Volume;1296 LNNS;pp.153-169
publisher: Springer Science and Business Media Deutschland GmbH
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 23673370; ISBN: 978-981963313-5;
language: English
type: Conference paper
coverage: Gupta R., Department of Computer Science and Engineering, Manipal University Jaipur, Rajasthan, Jaipur, India; Logeshwaran J., Department of Computer Science, Christ (Deemed to Be University), Karnataka, Bengaluru, India; Dhanasekaran S., Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, India; Kumar J., Department of Information Technology, ABES Engineering College, UP, Ghaziabad, India; Garg A., Department of Computer Science and Engineering, Manipal University Jaipur, Rajasthan, Jaipur, India