Abstract
As data generation increasingly takes place on devices without
a wired connection, Machine Learning (ML) related traffic will
be ubiquitous in wireless networks. Many studies have shown
that traditional wireless protocols are highly inefficient or unsustainable
to support ML, which creates the need for new wireless
communication methods. In this monograph, we give a comprehensive
review of the state-of-the-art wireless methods that are
specifically designed to support ML services over distributed
datasets. Currently, there are two clear themes within the literature,
analog over-the-air computation and digital radio resource
management optimized for ML. This survey gives an introduction
to these methods, reviews the most important works, highlights
open problems, and discusses application scenarios.
