This paper provides a comprehensive overview of network slicing in the context of smart factory networks. It highlights the potential benefits, recent advancements, and technical scenarios involved in implementing network slicing for smart manufacturing.
The paper first discusses the support for network slicing in 5G networks and its importance in the smart factory use case. It then reviews the recent progress in applying machine learning-based solutions to network slicing problems, identifying key limitations such as the need for real-world validation, handling complexity, and addressing scalability and uncertainty.
The technical scenarios covered include the requirements of various smart factory applications, the network elements involved, the importance of slice-aware radio resource management, the indoor factory radio propagation model, and the characteristics of industrial traffic. The paper also discusses the enabling technologies, such as network functions virtualization and software-defined networking, as well as the ongoing standardization efforts by 3GPP and other organizations.
Finally, the paper highlights the open research challenges in areas like time-sensitive networking, terminal mobility, strict quality of service requirements, integration with existing wired technologies, radio propagation, and slice-aware resource management. The authors emphasize the need for continuous adaptation and innovation to fully harness the transformative potential of network slicing for smart factories.
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