Toward an Intelligent Edge: Wireless Communication Meets Machine Learning

TLDR

The revival of AI, coupled with ubiquitous smart mobile gadgets and IoT devices, has spurred the emergence of edge learning—a field that merges wireless communication and machine learning to address limited computing power and data at edge devices. The article proposes learning‑driven communication guidelines for wireless communication in edge learning. The approach leverages mobile edge computing and distributed massive data across many edge devices to implement these guidelines. Illustrative examples demonstrate the effectiveness of the guidelines, and the article identifies unique research opportunities.

Abstract

The recent revival of AI is revolutionizing almost every branch of science and technology. Given the ubiquitous smart mobile gadgets and IoT devices, it is expected that a majority of intelligent applications will be deployed at the edge of wireless networks. This trend has generated strong interest in realizing an "intelligent edge" to support AI-enabled applications at various edge devices. Accordingly, a new research area, called edge learning, has emerged, which crosses and revolutionizes two disciplines: wireless communication and machine learning. A major theme in edge learning is to overcome the limited computing power, as well as limited data, at each edge device. This is accomplished by leveraging the mobile edge computing platform and exploiting the massive data distributed over a large number of edge devices. In such systems, learning from distributed data and communicating between the edge server and devices are two critical and coupled aspects, and their fusion poses many new research challenges. This article advocates a new set of design guidelines for wireless communication in edge learning, collectively called learning- driven communication. Illustrative examples are provided to demonstrate the effectiveness of these design guidelines. Unique research opportunities are identified.