Toward Autonomous Resource Management Architecture for 6G Satellite-Terrestrial Integrated Networks
Authors: 
Feng Ding, Chenxi Bao, Di Zhou, Min Sheng, Yan Shi, Jiandong LiAuthors Info & Claims
Abstract
Different from all existing mobile communication systems, the sixth-generation (6G) mobile communication system is expected to realize satellite-terrestrial integrated networks (STINs) and ubiquitous artificial intelligence (AI), which promotes that resource management (RM) realizes higher autonomy facing heterogeneous and high dynamic STIN and the 6G diverse service requirements. However, in what form will AI be applied in STIN’s RM to give full play to its capabilities? How to construct an AI-integrated STIN’s RM architecture to endow RM with higher autonomy and greater flexibility? In this article, AI is applied to STIN in an endogenous form, i.e., STIN has the ability to perceive and process information on service demands and resource states, and STIN can realize continuous optimization of service performance under unmanned conditions to play the role of AI more effectively. Further, this article combines the characteristics of AI and STIN to design an AI-centric threelevel closed-loop (Resource-Access-Service-Access-Resource) intelligent RM (RASAR) architecture for STIN. Specifically, the RASAR architecture abstracts the STIN’s RM into three independently deployable management functions and achieves satelliteterrestrial and access-bearer integrated resource management by designing the interfaces and the service performance feedback mechanism with the help of STIN’s transmission capability and computing power. In addition, the closed-loop implementation of RASAR architecture promotes the combination of AI selftraining and self-optimization of all levels to provide a solution for STIN’s autonomous RM. Finally, a case study is presented, followed by a discussion of open research issues that are essential for the RASAR architecture.
References
[1]
X. Zhuet al., “Cooperative transmission in integrated terrestrial-satellite networks,” IEEE Netw., vol. 33, no. 3, pp. 204–210, May 2019.
[2]
M. Shenget al., “Coverage enhancement for 6G satellite-terrestrial integrated networks: Performance metrics, constellation configuration and resource allocation,” Sci. China Inf. Sci., vol. 66, no. 3, pp. 1–20, Feb. 2023.
[3]
6G Network Architecture Vision and Key Technology Outlook White Paper, document IMT-2030, (6G) Network Working Group, Beijing, China, 2021.
[4]
N. Katoet al., “Ten challenges in advancing machine learning technologies toward 6G,” IEEE Wireless Commun., vol. 27, no. 3, pp. 96–103, Jun. 2020.
[5]
W. Wuet al., “AI-native network slicing for 6G networks,” IEEE Wireless Commun., vol. 29, no. 1, pp. 96–103, Feb. 2022.
[6]
S. Jiet al., “Flexible and distributed mobility management for integrated terrestrial-satellite networks: Challenges, architectures, and approaches,” IEEE Netw., vol. 35, no. 4, pp. 73–81, Jul. 2021.
[7]
W. Saad, M. Bennis, and M. Chen, “A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,” IEEE Netw., vol. 34, no. 3, pp. 134–142, May 2020.
[8]
C.-Q. Daiet al., “Distributed user association with grouping in satellite-terrestrial integrated networks,” IEEE Internet Things J., vol. 9, no. 12, pp. 10244–10256, Jun. 2022.
[9]
S. Fu, J. Gao, and L. Zhao, “Collaborative multi-resource allocation in terrestrial-satellite network towards 6G,” IEEE Trans. Wireless Commun., vol. 20, no. 11, pp. 7057–7071, Nov. 2021.
[10]
6G Network Native Ai Technical Requirement White Paper, document 6GANA, (6G) Network Working Group, 2022.
[11]
Y. Mansouri and M. A. Babar, “A review of edge computing: Features and resource virtualization,” J. Parallel Distrib. Comput., vol. 150, pp. 155–183, Apr. 2021.
[12]
J. Jiaoet al., “Massive access in space-based Internet of Things: Challenges, opportunities, and future directions,” IEEE Wireless Commun., vol. 28, no. 5, pp. 118–125, Oct. 2021.
[13]
F. Debbabiet al., “An overview of interslice and intraslice resource allocation in B5G telecommunication networks,” IEEE Trans. Netw. Service Manage., vol. 19, no. 4, pp. 5120–5132, Dec. 2022.
[14]
X. Shenet al., “AI-assisted network-slicing based next-generation wireless networks,” IEEE Open J. Veh. Technol., vol. 1, pp. 45–66, Jan. 2020.
[15]
T. K. Rodrigues and N. Kato, “Network slicing with centralized and distributed reinforcement learning for combined satellite/ground networks in a 6G environment,” IEEE Wireless Commun., vol. 29, no. 1, pp. 104–110, Feb. 2022.
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Published: 15 January 2024
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