A Review of the Convergence of 5G/6G Architecture and Deep Learning
- URL: http://arxiv.org/abs/2208.07643v1
- Date: Tue, 16 Aug 2022 10:05:19 GMT
- Title: A Review of the Convergence of 5G/6G Architecture and Deep Learning
- Authors: Olusola T. Odeyomi, Olubiyi O. Akintade, Temitayo O. Olowu, and
Gergely Zaruba
- Abstract summary: This paper provides an overview of the convergence of the key 5G technologies and deep learning.
In addition, a brief overview of the future 6G architecture, and how it can converge with deep learning is also discussed.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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