Scientific study of algorithms and statistical models that computer systems use to perform a specific task without using overt directions, but depending on the patterns and the inference instead is known as Machine Learning (ML). It is often referred to as a subgroup of artificial intelligence. A mathematical model is built by the ML algorithm, known as “training data”. This model is used to make predictions or decisions without being explicitly programmed to perform the task. Wide variety of applications such as email filtering and computer vision use ML algorithms, where it is problematic or infeasible to develop a conventional algorithm for effectively performing the task. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.

Machine learning and data driven approaches have recently received much attention as a key enabler for future 5G and beyond wireless networks. Yet, the evolution towards learning-based data driven networks is still in its infancy, and much of the realization of the promised benefits requires thorough research and development. Fundamental questions remain as to where and how ML can really complement the well-established, well-tested communication systems designed over the last 4 decades. Moreover, adaptation of machine learning methods is likely needed to realize their full potential in the wireless context. This is particularly challenging for the lower layers of the protocol stack, where the constraints, problem formulation, and even the objectives may fundamentally differ from the typical scenarios to which machine learning has been successfully applied in recent years. In addition, a deep understanding of the fundamental performance limits is also essential in order to establish quality-of-service guarantees that are common in communication system design. All such research challenges lie at the core of this special issue