Abstract
To achieve automation and intelligence in 5G networks, the 3rd Generation Partnership Project (3GPP) introduced the Network Data Analysis Function (NWDAF) as a novel network function. However, in the traditional 5G core network architecture, NWDAF relies on fixed configurations for data collection, lacking support for user customization and flexibility. Additionally, the current deployment of NWDAF is predominantly centralized, failing to provide real-time and reliable analysis services for the massive data in future 6G systems. Moreover, it is incapable of ensuring user privacy, making it incompatible with emerging scenarios like federated learning in 6G. Therefore, this paper proposes a user-customizable data collection approach and introduces a distributed NWDAF deployment based on the Raft algorithm, where the master node assigns data collection, analysis, and inference tasks to multiple worker NWDAFs. Our work and experimental results demonstrate that the proposed architecture effectively addresses these challenges and further achieves closed-loop network automation in 6G systems.
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References
3GPP: System architecture for the 5G System (5GS). Technical Specification (TS) 23.501, 3rd Generation Partnership Project (3GPP)
Jeon, Y., Jeong, H., Seo, S., Kim, T., Ko, H., Pack, S.: A distributed NWDAF architecture for federated learning in 5G. In: 2022 IEEE International Conference on Consumer Electronics (ICCE), pp. 1ā2 (2022). https://doi.org/10.1109/ICCE53296.2022.9730220
3GPP: Study of enablers for network automation for 5G. Technical Specification (TS) 23.791, 3rd Generation Partnership Project (3GPP)
3GPP: Architecture enhancements for 5G system (5GS) to support network data analytics services. Technical Specification (TS) 23.288, 3rd Generation Partnership Project (3GPP)
Ahmad, F.: Data collection for Machine Learning in 5G Mobile Networks. Masterās thesis, Oslomet-storbyuniversitetet (2023)
Li, J., et al.: 5GC network and MEC UPF data collection scheme research. In: 2021 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM), pp. 80ā85. IEEE (2021)
Li, P., Xing, Y., Li, W.: Distributed AI-native architecture for 6G networks. In: 2022 International Conference on Information Processing and Network Provisioning (ICIPNP), pp. 57ā62. IEEE (2022)
Yu, L., Xin, L., Guo, M.: A safe architecture of 5G network intelligence based on federated learning and NWDAF. In: Proceedings of the 4th International Conference on Advanced Information Science and System, pp. 1ā5 (2022)
Zhou, C., Ansari, N.: Securing federated learning enabled NWDAF architecture with partial homomorphic encryption. IEEE Network. Lett. (2023)
Jeon, Y., Pack, S.: Hierarchical network data analytics framework for B5G network automation: design and implementation. arXiv preprint arXiv:2309.16269 (2023)
Rajabzadeh, P., Outtagarts, A.: Federated learning for distributed NWDAF architecture. In: 2023 26th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN), pp. 24ā26. IEEE (2023)
Hossain, M.A., Hossain, A.R., Liu, W., Ansari, N., Kiani, A., Saboorian, T.: A distributed collaborative learning approach in 5G+ core networks. IEEE Netw., 1ā8 (2023). https://doi.org/10.1109/MNET.133.2200527
Lamport, L.: Paxos made simple. ACM SIGACT News (Distrib. Comput. Column) 32, 4 (Whole Number 121, December 2001), 51ā58 (2001)
Ongaro, D., Ousterhout, J.: In search of an understandable consensus algorithm. In: 2014 USENIX Annual Technical Conference (USENIX ATC 14), pp. 305ā319 (2014)
Castro, M., et al.: Practical byzantine fault tolerance. In: OsDI, vol. 99, pp. 173ā186 (1999)
Bamakan, S.M.H., Motavali, A., Bondarti, A.B.: A survey of blockchain consensus algorithms performance evaluation criteria. Expert Syst. Appl. 154, 113385 (2020)
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Ā© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Sun, W., Sun, Q. (2024). Distributed Intelligence Analysis Architecture forĀ 6G Core Network. In: Pan, L., Wang, Y., Lin, J. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2023. Communications in Computer and Information Science, vol 2062. Springer, Singapore. https://doi.org/10.1007/978-981-97-2275-4_30
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DOI: https://doi.org/10.1007/978-981-97-2275-4_30
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