Driven by the demand to accommodate today's growing mobile traffic, 5G is designed to be a key enabler and a leading infrastructure provider in the information and communication technology industry by supporting a variety of forthcoming services with diverse requirements. Considering the ever-increasing complexity of the network, and the emergence of novel use cases such as autonomous cars, industrial automation, virtual reality, e-health, and several intelligent applications, machine learning (ML) is expected to be essential to assist in making the 5G vision conceivable. This paper focuses on the potential solutions for 5G from an ML-perspective. First, we establish the fundamental concepts of supervised, unsupervised, and reinforcement learning, taking a look at what has been done so far in the adoption of ML in the context of mobile and wireless communication, organizing the literature in terms of the types of learning. We then discuss the promising approaches for how ML can contribute to supporting each target 5G network requirement, emphasizing its specific use cases and evaluating the impact and limitations they have on the operation of the network. Lastly, this paper investigates the potential features of Beyond 5G (B5G), providing future research directions for how ML can contribute to realizing B5G. This article is intended to stimulate discussion on the role that ML can play to overcome the limitations for a wide deployment of autonomous 5G/B5G mobile and wireless communications

Chapter Contents:

• 3.1 Introduction
• 3.1.1 AI-based wireless networks
• 3.2 Integration into wireless networks: smart radio environments
• 3.2.1 The role of deep learning in smart radio environments
• 3.2.2 ANNs deployment into wireless networks
• 3.3 State-of-the-art review
• 3.4 Energy efficiency optimization by deep learning
• 3.4.1 Weighted sum energy efficiency maximization
• 3.4.2 Energy efficiency in non-Poisson wireless networks: a deep transfer learning approach
• 3.5 Conclusions
• References

Inspec keywords: energy conservation; computer networks; neural nets; learning (artificial intelligence)

Other keywords: deep learning; machine learning; wireless communication; unsupervised learning; supervised learning; energy-efficient beyond 5G networks; reinforcement learning

Subjects: Computer communications; Computer networks and techniques; Neural computing techniques