Toward Native Artificial Intelligence in 6G Networks: System Design, Architectures, and Paradigms

R. Li, Z. Zhao, X. Zhou, G. Ding, Y. Chen, Z. Wang, and H. Zhang, “Intelligent 5G: When cellular networks meet artificial intelligence,” IEEE Wireless Commun. , vol. 24, no. 5, pp. 175–183, Oct. 2017

2017

Earlier work this paper cites.

E. Vinitsky, A. Kreidieh, L. L. Flem, N. Kheterpal, K. Jang, C. Wu, F. Wu, R. Liaw, E. Liang, and A. M. Bayen, “Benchmarks for reinforcement learning in mixed-autonomy traffic,” in CoRL 2018 , Zürich, Switzerland, Oct. 2018, pp. 399–409

Original

2018

Earlier work this paper cites.

D. Bega, M. Gramaglia, R. Perez, M. Fiore, A. Banchs, and X. Costa-Perez, “AI-based autonomous control, management, and orchestration in 5G: From standards to algorithms,” IEEE Network , vol. 34, no. 6, pp. 14–20, 2020

2020

Earlier work this paper cites.

R. Li, Z. Zhao, X. Xu, F. Ni, and H. Zhang, “The collective advantage for advancing communications and intelligence,” IEEE Wireless Commun. , vol. 27, no. 4, pp. 96–102, Aug. 2020

2020

Cited alongside, same era.

G. Zhu, D. Liu, Y. Du, C. You, J. Zhang, and K. Huang, “Toward an intelligent edge: Wireless communication meets machine learning,” IEEE Communications Magazine , vol. 58, no. 1, pp. 19–25, 2020

2020

Cited alongside, same era.

“3GPP TS 23.502. Technical specification group service and system aspects; system architecture for the 5G system (5GS),” v16.7.0

Cited in the paper.

“ITU Focus group on machine learning for future networks including 5G,” https://www.itu.int/en/ITU-T/focusgroups/ml5g/

Cited in the paper.

“Finnish 6G flagship white papers,” https://www.6gchannel.com/6g-white-papers/

2021

Closest in time.

X. Xu, R. Li, Z. Zhao, and H. Zhang, “Stigmergic independent reinforcement learning for multi-agent collaboration,” IEEE Trans. Neural Netw. Learn. Syst. , Feb. 2021

2021

Closest in time.