Abstract
Rapid development of wireless technologies, such as the Internet of Things (IoT) and widely deployed fifth generation (5G) networks, are proved to be the embarkment of envisioning, and planning for sixth generation (6G) mobile networks. It is believed that 6G will provide extremely high data rates, low latency, and improved edge intelligence for hundreds of billions of end devices, connected to the 6G network. Thus, a huge amount of data will be generated from these devices, which requires tremendous computation and communication resources to be provided by edge servers. The gap between users’ requirements and edge servers’ capability of service provisions in 6G systems is mitigated with digital twin with close monitoring, real-time interaction, and reliable communication between the digital space and the physical systems, which can in turn optimize the running of the physical systems. This article entails the basic knowledge of Digital Twin (DT) for 6G Wireless networks. Moreover, the properties of Federated Learning that can enhance the DT for 6G to provide high level performance in communication by presenting the case studies of smart manufacturing and smart cities.
Original language | English |
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Title of host publication | Digital Twins for Wireless Networks |
Publisher | Springer Nature |
Chapter | 5 |
Pages | 93-122 |
Publication status | Published - 2025 |