, NGMN alliance 5G white paper, 2015.

S. Buzzi, C. I. , T. E. Klein, H. V. Poor, C. Yang et al., A survey of energy-efficient techniques for 5g networks and challenges ahead, IEEE Journal on Selected Areas in Communications, vol.34, issue.5, 2016.

A. Zappone and E. Jorswieck, Energy efficiency in wireless networks via fractional programming theory, Foundations and Trends R in Communications and Information Theory, vol.11, issue.3-4, pp.185-396, 2015.

D. W. Ng, E. S. Lo, and R. Schober, Energy-efficient resource allocation in multi-cell OFDMA systems with limited backhaul capacity, IEEE Transactions on Wireless Communications, vol.11, issue.10, pp.3618-3631, 2012.

Q. Xu, X. Li, H. Ji, and X. Du, Energy-efficient resource allocation for heterogeneous services in OFDMA downlink networks: Systematic perspective, IEEE Transactions on Vehicular Technology, vol.63, issue.5, pp.2071-2082, 2014.

J. Xu and L. Qiu, Energy efficiency optimization for MIMO broadcast channels, IEEE Transactions on Wireless Communications, vol.12, issue.2, pp.690-701, 2013.

B. Du, C. Pan, W. Zhang, and M. Chen, Distributed energy-efficient power optimization for CoMP systems with max-min fairness, IEEE Communications Letters, vol.18, issue.6, pp.999-1002, 2014.

S. He, Y. Huang, L. Yang, and B. Ottersten, Coordinated multicell multiuser precoding for maximizing weighted sum energy efficiency, IEEE Transactions on Signal Processing, vol.62, issue.3, pp.741-751, 2014.
DOI : 10.1109/tsp.2013.2294595

A. Zappone, L. Sanguinetti, G. Bacci, E. A. Jorswieck, and M. Debbah, Energy-efficient power control: A look at 5G wireless technologies, IEEE Transactions on Signal Processing, vol.64, issue.7, pp.1668-1683, 2016.
DOI : 10.1109/tsp.2015.2500200

URL : https://hal.archives-ouvertes.fr/hal-01789320

A. Zappone, E. Björnson, L. Sanguinetti, and E. Jorswieck, Globally optimal energy-efficient power control and receiver design in wireless networks, IEEE Transactions on Signal Processing, vol.65, issue.11, pp.2844-2859, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01781867

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 2016.

T. O'shea and J. Hoydis, An introduction to deep learning for the physical layer, IEEE Transactions on Cognitive Communications and Networking, vol.3, issue.4, pp.563-575, 2017.

M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, Machine learning for wireless networks with artificial intelligence: A tutorial on neural networks, 2017.

D. Neumann, T. Wiese, and W. Utschick, Learning the MMSE channel estimator, 2017.

J. Vieira, E. Leitinger, M. Sarajlic, X. Li, and F. Tufvesson, Deep convolutional neural networks for massive MIMO fingerprint-based positioning, 2017.

N. Farsad and A. Goldsmith, Detection algorithms for communication systems using deep learning, 2017.

F. D. Calabrese, L. Wang, E. Ghadimi, G. Peters, and P. Soldati, Learning radio resource management in 5G networks: Framework, opportunities and challenges, 2017.

J. Fang, X. Li, W. Cheng, Z. Chen, and H. Li, Intelligent power control for spectrum sharing: A deep reinforcement learning approach, 2017.

H. Sun, X. Chen, Q. Shi, M. Hong, X. Fu et al., Learning to optimize: Training deep neural networks for wireless resource management, 2017.

K. Hornik, M. Stinchcombe, and H. White, Multilayer feedforward networks are universal approximators, Neural Networks, vol.2, issue.5, pp.359-366, 1989.

G. Calcev, D. Chizhik, B. Goransson, S. Howard, H. Huanga et al., A wideband spatial channel model for system-wide simulations, IEEE Transactions on Vehicular Technology, vol.56, issue.2, 2007.

E. Björnson, J. Hoydis, and L. Sanguinetti, Massive MIMO networks spectral, energy, and hardware efficiency, Foundations and Trends in Signal Processing, 2017.