Analysis of Data Science and AI-enabled 6G Wireless Communication Networks

Analysis of Data Science and AI-enabled 6G Wireless Communication Networks

Current networks (such as 4G and the forthcoming 5G networks) may not be capable of fully congregating quickly emerging traffic strains due to the proliferation of smart fatal, infrastructures and the explosion of diverse applications with varying necessities. As a result, 6G network research has al...

Full description

Saved in:
Bibliographic Details
Published in:Radioelectronics and communications systems Vol. 66; no. 5; pp. 223 - 232
Main Authors: Nancharaiah, Battula, Ravi, Kiran Chand, Srivastava, Ajeet Kumar, Arunkumar, K., Siddiqui, Shams Tabrez, Arun, M. R.
Format: Journal Article
Language:English
Published: Moscow Pleiades Publishing 2023
Springer Nature B.V
Subjects:
6G mobile communication
Algorithms
Artificial intelligence
Communication networks
Communications Engineering
Data analysis
Data science
Design optimization
Edge computing
Energy management
Engineering
Mobile computing
Networks
Provisioning
Resource management
Wireless communications
Wireless networks
AI
vision
network security
data science
big data
6G networks
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Current networks (such as 4G and the forthcoming 5G networks) may not be capable of fully congregating quickly emerging traffic strains due to the proliferation of smart fatal, infrastructures and the explosion of diverse applications with varying necessities. As a result, 6G network research has already seen participation from both the private sector and the academic community. Recently, an innovative paradigm has emerged for the intelligent design and optimization of 6G networks based on the combination of artificial intelligence (AI) and data science (DS). Therefore, this article proposes an AI-enabled architecture for 6G networks, which is alienated into four layers: intelligent sensing, data analytics, intelligent control, and smart application, to realize patterns sighting, smart resource management, automatic network adjustment, and intelligent service provisioning. We go over the uses of DS&AI methods in 6G networks, such as AI-enhanced mobile edge computing, intelligent mobility, and smart-spectrum management, and how to implement these methods to maximize the network’s performance. We also emphasize key areas for future study and clarifications for AI-enabled 6G networks, together with computational efficiency, algorithm resilience, hardware development, and energy management.
ISSN:0735-2727
1934-8061
DOI:10.3103/S0735272723050059