The Convergence of Machine Learning and Communications
This is an incremental survey paper for researchers in communications and machine learning.
The paper provides an overview of how machine learning can enhance communication systems by leveraging traffic data and end-to-end training, and discusses two applications in wireless networking.
The areas of machine learning and communication technology are converging. Today's communications systems generate a huge amount of traffic data, which can help to significantly enhance the design and management of networks and communication components when combined with advanced machine learning methods. Furthermore, recently developed end-to-end training procedures offer new ways to jointly optimize the components of a communication system. Also in many emerging application fields of communication technology, e.g., smart cities or internet of things, machine learning methods are of central importance. This paper gives an overview over the use of machine learning in different areas of communications and discusses two exemplar applications in wireless networking. Furthermore, it identifies promising future research topics and discusses their potential impact.