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.INDEX TERMS Machine learning, 5G mobile communication, B5G, wireless communication, mobile communication, artificial intelligence.
II. THE THREE TYPES OF LEARNING AND ITS APPLICATION IN WIRELESS COMMUNICATIONSThe article is divided according to the level of supervision that the ML procedure requires on the training stage. The major categories discussed in the following sections are supervised, unsupervised, and reinforcement learning
Multi-hop relay selection is a critical issue in vehicleto-everything networks. In previous works, the optimal hopping strategy is assumed to be based on the shortest distance. This study proposes a hopping strategy based on the lowest propagation loss, considering the effect of the environment. We use a twostep machine learning routine: improved deep encoder-decoder architecture to generate environmental maps and Q-learning to search for the multi-hopping path with the lowest propagation loss. Simulation results show that our proposed method can improve environmental recognition and extend the reachability of multi-hop communications by up to 66.7%, compared with a shortest-distance selection.
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