An overview of the platform in Chinese.
An English introduction of the platform in IEEE INFOCOM 2022.
Other Relevant Publications:
- Y. Hua, R. Li, Z. Zhao, X. Chen, and H. Zhang, “GAN-powered deep distributional reinforcement learning for resource management in network slicing,” IEEE J. Sel. Area. Comm., vol. 38, no. 2, pp. 334–349, Feb. 2020.
- Y. Shao, R. Li, B. Hu, Y. Wu, Z. Zhao, and H. Zhang, “Graph attention network-based multi-agent reinforcement learning for slicing resource management in dense cellular network,” IEEE Trans. Veh. Tech., vol. 70, no. 10, pp. 10792–10803, Oct. 2021.
- R. Li, C. Wang, R. Guo, Z. Zhao, and H. Zhang, “The LSTM-based advantage actor-critic learning for resource management in network slicing with user mobility,” IEEE Commun. Lett., vol. 24, no. 9, pp. 2005–2009, Sep. 2020.
- C. Qi, Y. Hua, R. Li, Z. Zhao, and H. Zhang, “Deep reinforcement learning with discrete normalized advantage functions for resource management in network slicing,” IEEE Commun. Lett., vol. 23, no. 6, pp. 1337–1341, Aug. 2019.
- R. Li et al., “Deep reinforcement learning for resource management in network slicing,” IEEE Access, vol. 6, pp. 74429–74441, Nov. 2018.
- X. Zhou, R. Li, T. Chen, and H. Zhang, “Network slicing as a service: Enable industries own software-defined cellular networks,” IEEE Commun. Mag., vol. 54, no. 7, pp. 146–153, Jul. 2016.
- R. Li et al., “Intelligent 5G: When cellular networks meet artificial intelligence,” IEEE Wireless Commun., vol. 24, no. 5, pp. 175–183, 2017.
Relevant Codes on Github