Towards an Intelligent Edge: Wireless Communication Meets Machine Learning

TL;DR

This paper explores the integration of wireless communication and machine learning at the network edge, proposing new design principles to enhance AI-enabled applications on resource-constrained edge devices.

Contribution

It introduces the concept of learning-driven communication, bridging wireless communication and edge learning, and highlights new research opportunities in this emerging field.

Findings

Proposes learning-driven communication principles.

Demonstrates effectiveness through illustrative examples.

Identifies key research challenges and opportunities.

Abstract

The recent revival of artificial intelligence (AI) is revolutionizing almost every branch of science and technology. Given the ubiquitous smart mobile gadgets and Internet of Things (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 interests in realizing an "intelligent edge" to support AI-enabled applications at various edge devices. Accordingly, a new research area, called edge learning, emerges, 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 (MEC) 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 principles for wireless communication in edge learning, collectively called learning-driven communication. Illustrative examples are provided to demonstrate the effectiveness of these design principles, and unique research opportunities are identified.