**6. Conclusion**

After building 4G network, network operators offered real broadband mobile Internet with capacities up to 600 Mbps. However, services and subscribers requirements have evolved to a very demanding and unprecedented level for higher quality of service. Virtual reality and augmented reality are demanding extreme high capacities and Internet of vehicles are requiring ultra-reliable communication and extreme low latency. This pushed the mobile network operators to start the migration toward 5G. Indeed, evolved technologies allowed 5G to reach bit rates over 1Gbps, through new radio interface, massive MIMO, beamforming, etc. However, the operators has to increase also the intelligence in their network, to learn more concisely about their operating environment and forecasting its evolution to optimize the resources utilization, adapt and configure automatically the network to cope with the wide variety of services. In this chapter, we have shown that this became possible through the integration of artificial intelligence and different machine learning approaches. We have presented some interesting use cases, which allow the operator to build self-healing and selfupgrading networks.

**65**

**Author details**

Morocco

Abdelfatteh Haidine1

\*, Fatima Zahra Salmam<sup>2</sup>

2 Faculty of Sciences, Chouaib Doukkali University, El Jadida, Morocco

\*Address all correspondence to: haidine.a@ucd.ac.ma

provided the original work is properly cited.

1 National School of Applied Sciences, Chouaib Doukkali University, El Jadida,

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

, Abdelhak Aqqal1

and Aziz Dahbi1

*Artificial Intelligence and Machine Learning in 5G and beyond: A Survey and Perspectives*

4G/5G Fourth/fifth generation of mobile network

M2M Machine-to-Machine communication

mMTC Massive Machine-Type Communications

OFDM Orthogonal frequency Division Multiplexing

RRM/RRA Radio Resource Management/Allocation

*DOI: http://dx.doi.org/10.5772/intechopen.98517*

AI Artificial Intelligence CAPEX Capital Expenditure Gbps Giga bit per second. LTE Long Term Evolution

MAC Medium Access Control MIMO Multi-Input Multi-Output

MNO Mobile Network Operator NB-IoT Narrowband Internet of Things NFV Network Function Virtualization NOMA Non-Orthogonal Multiple Access

OPEX Operational Expenditure QoE Quality of Experience QoS Quality of Service

SDN Software-Defined network

ML Machine Learning

**Nomenclature**

*Artificial Intelligence and Machine Learning in 5G and beyond: A Survey and Perspectives DOI: http://dx.doi.org/10.5772/intechopen.98517*
