Reinforcement Learning for QoS-Constrained Autonomous Resource Allocation with H2H/M2M Co-Existence in Cellular Networks

Author:

WEI Xing1,LI Xuehua1,CHEN Shuo1,LI Na2

Affiliation:

1. Key Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science and Technology University

2. Baicells Technologies, Co., Ltd.

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Software

Reference27 articles.

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2. [2] F. Boccardi, R.W. Heath, A. Lozano, T.L. Marzetta, and P. Popovski, “Five disruptive technology directions for 5G,” IEEE Commun. Mag., vol.52, no.2, pp.74-80, 2014. 10.1109/mcom.2014.6736746

3. [3] M.K. Giluka, N.S. Kumar, N. Rajoria, and B.R. Tamma, “Class based priority scheduling to support machine to machine communications in LTE systems,” 2014 Twentieth National Conference on Communications (NCC), pp.1-6, IEEE, 2014. 10.1109/ncc.2014.6811381

4. [4] Z.J. Yang, J.C. Huang, C.T. Chou, H.Y. Hsieh, C.W. Hsu, P.C. Yeh, and C.C.A. Hsu, “Peer discovery for device-to-device (D2D) communication in LTE-A networks,” 2013 IEEE Globecom Workshops (GC Wkshps), pp.665-670, IEEE, 2013. 10.1109/glocomw.2013.6825064

5. [5] F. Ghavimi and H.H. Chen, “M2M communications in 3GPP LTE/LTE-A networks: Architectures, service requirements, challenges, and applications,” IEEE Commun. Surveys Tuts., vol.17, no.2, pp.525-549, 2014. 10.1109/comst.2014.2361626

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