Machine Learning in the Air

TLDR

Recent advances in processing speed, data acquisition, and storage have enabled machine learning to permeate many domains, including wireless communications, yet its practical impact on communication systems and standards remains uncertain. This paper reviews the major promises and challenges of machine learning for the physical layer of wireless communication systems, highlighting recent accomplishments and outlining promising future research directions. We emphasize the complementary challenge of designing physical‑layer techniques that enable distributed machine learning at the wireless network edge, underscoring the need to integrate ML with fundamental wireless concepts.

Abstract

Thanks to the recent advances in processing speed, data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story - ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles. While recent years have seen a flurry of research activity in exploiting ML tools for various wireless communication problems, the impact of these techniques in practical communication systems and standards is yet to be seen. In this paper, we review some of the major promises and challenges of ML in wireless communication systems, focusing mainly on the physical layer. We present some of the most striking recent accomplishments that ML techniques have achieved with respect to classical approaches, and point to promising research directions where ML is likely to make the biggest impact in the near future. We also highlight the complementary problem of designing physical layer techniques to enable distributed ML at the wireless network edge, which further emphasizes the need to understand and connect ML with fundamental concepts in wireless communications.