Pith. sign in

REVIEW

Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2001.08159 v1 pith:XSBZD6K3 submitted 2020-01-02 cs.NI eess.SP

Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges

classification cs.NI eess.SP
keywords networkswirelesschallengesfutureadvancesalgorithmsapplyingartificial
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

The fifth generation (5G) wireless communication networks are currently being deployed, and beyond 5G (B5G) networks are expected to be developed over the next decade. Artificial intelligence (AI) technologies and, in particular, machine learning (ML) have the potential to efficiently solve the unstructured and seemingly intractable problems by involving large amounts of data that need to be dealt with in B5G. This article studies how AI and ML can be leveraged for the design and operation of B5G networks. We first provide a comprehensive survey of recent advances and future challenges that result from bringing AI/ML technologies into B5G wireless networks. Our survey touches different aspects of wireless network design and optimization, including channel measurements, modeling, and estimation, physical-layer research, and network management and optimization. Then, ML algorithms and applications to B5G networks are reviewed, followed by an overview of standard developments of applying AI/ML algorithms to B5G networks. We conclude this study by the future challenges on applying AI/ML to B5G networks.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.