Green resource management in 6G: AI-enabled approaches for energy-efficient wireless networks

Author First name, Last name, Institution

• Deepro Bhattacharyya, Kalinga Institute of Industrial Technology, Bhubaneswar
• Ali Ahmad, Kalinga Institute of Industrial Technology, Bhubaneswar
• Vandana Sharma, Christ University
• Iqra Husain, Zayed University, Abu Dhabi Campus
• Pradeep Hewage, University of Greater Manchester
• Celestine Iwendi, University of Greater Manchester

Document Type

Conference Proceeding

Source of Publication

Aip Conference Proceedings

Publication Date

5-21-2026

Abstract

The development of the sixth generation (6G) networks comes with new and improved features such as; fast data transfer speed, low latency and many connected devices. However these features come with a cost as the power consumption is greatly increased thus posing a big problem to the network's sustainability. Energy efficiency has become a key issue as 6G is to support various applications such as IoT enabled automation, energy real such saving time as techniques multimedia terahertz are services communications not and intelligent efficient transportation intelligent and networks. reflecting effective. This surfaces for is because that the 6G are dynamic systems used nature which in of are the conventional systems, system diversity. The in use devices of and technology Artificial since Intelligence they (AI) can and enable Machine the Learning development (ML) of is intelligent, therefore adaptive seen and as self-regulatory the energy solution management of systems a for kind 6G networks. The role of AI and ML in improving the energy efficiency of 6G networks is discussed in this paper, with a focus on solutions for dynamic resource management, power optimization, and traffic flow control. In addition, the problems of computational costs and their scalability, privacy threats, and the tradeoff between energy efficiency and network performance are also presented. The results show that there is the need for new AI-based approaches to develop sustainable and energy efficient 6G networks and advance green communication technologies.

DOI Link

10.1063/5.0329034

ISSN

0094-243X

Publisher

AIP Publishing

Volume

3410

Issue

1

Disciplines

Computer Engineering

Keywords

6G Networks, Artificial Intelligence (AI), Dynamic Resource Allocation, Energy Efficiency, Machine Learning (ML)

Scopus ID

105040759634

Recommended Citation

Bhattacharyya, Deepro; Ahmad, Ali; Sharma, Vandana; Husain, Iqra; Hewage, Pradeep; and Iwendi, Celestine, "Green resource management in 6G: AI-enabled approaches for energy-efficient wireless networks" (2026). All Works. 8124.
https://zuscholars.zu.ac.ae/works/8124

Indexed in Scopus

yes

Open Access

no