, Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2016.

E. Gelenbe and Y. Caseau, The impact of information technology on energy consumption and carbon emissions, Ubiquity, 2015.

B. Debaillie, C. Desset, and F. Louagie, A flexible and future-proof power model for cellular base stations, 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), pp.1-7, 2015.

L. Suarez, L. Nuaymi, and J. Bonnin, An overview and classification of research approaches in green wireless networks, EURASIP Journal on Wireless Communications and Networking, vol.2012, issue.1, p.142, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00709409

, On requirements and design of ss burst set and ss block index indication, TS 38.300 Release 15, 2017.

J. Wu, Y. Zhang, M. Zukerman, and E. K. Yung, Energy-efficient basestations sleep-mode techniques in green cellular networks: A survey, IEEE Communications Surveys Tutorials, vol.17, issue.2, pp.803-826, 2015.

M. Feng, S. Mao, and T. , Base station ON-OFF switching in 5G wireless networks: Approaches and challenges, vol.24, pp.46-54, 2017.

, Air interface for fixed and mobile broadband wireless access systems amendment 2: Physical and medium access control layers for combined fixed and mobile operation in licensed bands and corrigendum 1, IEEE Std, vol.16, 2006.

J. Peng, P. Hong, and K. Xue, Stochastic analysis of optimal base station energy saving in cellular networks with sleep mode, IEEE Communications Letters, vol.18, issue.4, pp.612-615, 2014.

M. Feng, S. Mao, and T. Jiang, Boost: Base station ON-OFF switching strategy for energy efficient massive MIMO hetnets, IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, pp.1-9, 2016.

X. Guo, Z. Niu, S. Zhou, and P. R. Kumar, Delay-constrained energyoptimal base station sleeping control, IEEE Journal on Selected Areas in Communications, vol.34, issue.5, pp.1073-1085, 2016.

Y. Che, L. Duan, and R. Zhang, Dynamic base station operation in large-scale green cellular networks, IEEE Journal on Selected Areas in Communications, issue.99, pp.1-1, 2016.

C. Liu, B. Natarajan, and H. Xia, Small cell base station sleep strategies for energy efficiency, IEEE Transactions on Vehicular Technology, vol.65, issue.3, pp.1652-1661, 2016.

P. Lähdekorpi, M. Hronec, P. Jolma, and J. Moilanen, Energy efficiency of 5G mobile networks with base station sleep modes, 2017 IEEE Conference on Standards for Communications and Networking (CSCN), pp.163-168, 2017.

F. E. Salem, A. Gati, Z. Altman, and T. Chahed, Advanced sleep modes and their impact on flow-level performance of 5G networks, 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), pp.1-7, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01711991

S. Fatma-ezzahra, Reinforcement learning approach for advanced sleep modes management in 5G networks, 2018 IEEE Vehicular Technology Conference (VTC-Fall), 2018.

Y. Bengio, A. Courville, and P. Vincent, Representation learning: A review and new perspectives, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.8, pp.1798-1828, 2013.

C. Jiang, H. Zhang, Y. Ren, Z. Han, K. Chen et al., Machine learning paradigms for next-generation wireless networks, vol.24, pp.98-105, 2017.

Q. Zhao and D. Grace, Transfer learning for QoS aware topology management in energy efficient 5G cognitive radio networks, 1st International Conference on 5G for Ubiquitous Connectivity, pp.152-157, 2014.

M. Miozzo, L. Giupponi, M. Rossi, and P. Dini, Distributed QLearning for energy harvesting heterogeneous networks, 2015 IEEE International Conference on Communication Workshop (ICCW), pp.2006-2011, 2015.

H. Li, H. Gao, T. Lv, and Y. Lu, Deep Q-Learning based dynamic resource allocation for self-powered ultra-dense networks, 2018 IEEE International Conference on Communications Workshops (ICC Workshops), pp.1-6, 2018.

R. S. Sutton and A. G. Barto, Introduction to Reinforcement Learning, 1998.

, IMEC power model tool