Toward Sustainable 6G Leveraging Digital Twin and Artificial Intelligence: Framework and Case Study
Year: 2025
Type: article
Abstract: The forthcoming 6G networks will adopt higher frequency bands, ultra-dense base station deployments, and advanced technologies, such as massive MIMO and edge intelligence, to achieve enhanced bandwidth, connectivity, and intelligence. However, these advancements will lead to significant increases in energy consumption. In this article, we propose to integrate digital twin and artificial intelligence to systematically solve the problem of energy sustainability in 6G networks. We explore the key challenges of integrating these two technologies and design an intelligent digital twin framework to address them. Our framework adopts a modular design, encompassing modeling, simulation, optimization, and longterm evolution modules. As a case study, we conduct a preliminary implementation of the framework in the 5th Ring Road area of Beijing, China, demonstrating its effectiveness in improving energy efficiency. Experimental results show that our framework significantly reduces the energy consumption of 6G networks while optimizing resource allocation to reduce computing latency.
Source: IEEE Communications Magazine
Authors: Dianlei Xu, Xiang Su, Sasu Tarkoma, Pan Hui
Institution: University of Helsinki
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4408358268