A Review of the Convergence of 5G/6G Architecture and Deep Learning

TL;DR

This paper provides a comprehensive review of how deep learning converges with 5G technologies, discusses associated challenges, and explores future 6G integration, filling gaps left by previous surveys.

Contribution

It offers a structured overview of the full spectrum of 5G architecture and deep learning convergence, including challenges and future 6G prospects.

Findings

Identifies key 5G technologies that integrate with deep learning.

Discusses challenges faced in convergence.

Provides insights into future 6G architecture and deep learning integration.

Abstract

The convergence of 5G architecture and deep learning has gained a lot of research interests in both the fields of wireless communication and artificial intelligence. This is because deep learning technologies have been identified to be the potential driver of the 5G technologies, that make up the 5G architecture. Hence, there have been extensive surveys on the convergence of 5G architecture and deep learning. However, most of the existing survey papers mainly focused on how deep learning can converge with a specific 5G technology, thus, not covering the full spectrum of the 5G architecture. Although there is a recent survey paper that appears to be robust, a review of that paper shows that it is not well structured to specifically cover the convergence of deep learning and the 5G technologies. Hence, this paper provides a robust overview of the convergence of the key 5G technologies and deep learning. The challenges faced by such convergence are discussed. In addition, a brief overview of the future 6G architecture, and how it can converge with deep learning is also discussed.