Device Association Based on Federated Deep Reinforcement Learning for Radio Access Network Slicing

Author:

Liu Yi‐Jing,Feng Gang,Sun Yao,Qin Shuang

Publisher

Wiley

Reference22 articles.

1. MingzheChen ZhaohuiYang WalidSaad ChangchuanYin H. VincentPoor andShuguangCui.A joint learning and communications framework for federated learning over wireless networks.arXiv preprint arXiv:1909.07972 2019.

2. 6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions

3. Ericsson AB.5G Systems –enabling the transformation of industry and society.White Paper (January):14 2017. URLhttps://www.ericsson.com/res/docs/whitepapers/wp‐5g‐systems.pdf.

4. ETSI.TS 136 331 ‐ V15.3.0 ‐ LTE; Evolved Universal Terrestrial Radio Access (E‐UTRA); Radio Resource Control (RRC); Protocol specification.3GPP TS 36.331 version 13.7.1 Release 13 pages1:1–1:649 2018. URLhttps://portal.etsi.org/TB/ETSIDeliverableStatus.aspx.

5. Design and Evaluation of a Backhaul-Aware Base Station Assignment Algorithm for OFDMA-Based Cellular Networks

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