Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Information Theory

arXiv:2109.11320 (cs)
[Submitted on 23 Sep 2021]

Title:Nine Challenges in Artificial Intelligence and Wireless Communications for 6G

Authors:Wen Tong, Geoffrey Ye Li
View PDF
Abstract:In recent years, techniques developed in artificial intelligence (AI), especially those in machine learning (ML), have been successfully applied in various areas, leading to a widespread belief that AI will collectively play an important role in future wireless communications. To accomplish the aspiration, we present nine challenges to be addressed by the interdisciplinary areas of AI/ML and wireless communications, with particular focus towards the sixth generation (6G) wireless networks. Specifically, this article classifies the nine challenges into computation in AI, distributed neural networks and learning, and ML enabled semantic communications.
Comments: 6 pages
Subjects: Information Theory (cs.IT)
Cite as: arXiv:2109.11320 [cs.IT]
  (or arXiv:2109.11320v1 [cs.IT] for this version)
  https://doi.org/10.48550/arXiv.2109.11320

Submission history

From: Geoffrey Li [view email]
[v1] Thu, 23 Sep 2021 12:07:26 UTC (618 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
license icon view license
Current browse context:
cs.IT
< prev   |   next >
new | recent | 2021-09
Change to browse by:
cs
math
math.IT

References & Citations

DBLP - CS Bibliography

listing | bibtex
Wen Tong
Geoffrey Ye Li
export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)