Abstract
Artificial Intelligence (AI) and especially Machine Learning (ML) can play a very important role in realizing and optimizing 6G network applications. In this paper, we present a brief summary of ML methods, as well as an up-to-date review of ML approaches in 6G wireless communication systems. These methods include supervised, unsupervised and reinforcement techniques. Additionally, we discuss open issues in the field of ML for 6G networks and wireless communications in general, as well as some potential future trends to motivate further research into this area.
Highlights
Wireless communication systems have experienced substantial revolutionary progress over the past years
Machine Learning (ML) models are computational systems that are able to learn the features of a system that cannot be represented by using a conventional mathematical model approach
After the model is trained on the given training data-set, it can be effectively applied to unknown data and Electronics 2021, 10, 2786 perform any decision based on the training data
Summary
Wireless communication systems have experienced substantial revolutionary progress over the past years. With the rapid progress of 3GPP 5G phase 2 standardization, the commercial deployment of 5G applications being deployed all over the world cannot fully meet the challenges brought by the rapid increase of traffic and the real-time requirement of services [1]. In this behalf, industry and academia are already working towards realizing the sixth generation (6G) communication systems. Holographic-Type Communication (HTC), Tactile Internet, Intelligent Transport and Logistics, Intelligent and automated machines, Virtual Reality (VR), Augmented Reality (AR), Extended reality (XR)
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