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Cited by 372

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Machine Learning for 6G Wireless Networks: Carrying Forward Enhanced Bandwidth, Massive Access, and Ultrareliable/Low-Latency Service

Du, Jun, Jiang, Chunxiao, Wang, Jian, Ren, Yong, Debbah, Merouane·2020·IEEE Vehicular Technology Magazine
DOI 10.1109/mvt.2020.3019650
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Abstract

To satisfy the expected plethora of demanding services, the future generation of wireless networks (6G) has been mandated as a revolutionary paradigm to carry forward the capacities of enhanced broadband, massive access, and ultrareliable and lowlatency service in 5G wireless networks to a more powerful and intelligent level. Recently, the structure of 6G networks has tended to be extremely heterogeneous, densely deployed, and dynamic. Combined with tight quality of service (QoS), such complex architecture will result in the untenability of legacy network operation routines. In response, artificial intelligence (AI), especially machine learning (ML), is emerging as a fundamental solution to realize fully intelligent network orchestration and management. By learning from uncertain and dynamic environments, AI-/ML-enabled channel estimation and spectrum management will open up opportunities for bringing the excellent performance of ultrabroadband techniques, such as terahertz communications, into full play. Additionally, challenges brought by ultramassive access with respect to energy and security can be mitigated by applying AI-/ML-based approaches. Moreover, intelligent mobility management and resource allocation will guarantee the ultrareliability and low latency of services. Concerning these issues, this article introduces and surveys some state-of-the-art techniques based on AI/ML and their applications in 6G to support ultrabroadband, ultramassive access, and ultrareliable and lowlatency services.

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Research works printed but not linked: 40

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Metadata

Authors
Du, J., Jiang, C., Wang, J., Ren, Y., & Debbah, M.
Venue
IEEE Vehicular Technology Magazine
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020-12
Volume
15
Issue
4
Pages
122-134
ISSN
  • 1556-6072 (ISSN-L)
  • 1556-6080
DOI
10.1109/mvt.2020.3019650
OpenAlex
W3089283090
Cited by
372
References
6
FWCI
15.70
Type
journal article
Language
en
Open access
closed
Licence
Funders
  • National Natural Science Foundation of China (61971257)
  • China Postdoctoral Science Foundation (2019T120091; 2018M640130)
  • The Verification Platform of Multi-tier Coverage Communication Network for Oceans (LZC0020)

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CoChat. (2026). Citation check: Machine Learning for 6G Wireless Networks: Carrying Forward Enhanced Bandwidth, Massive Access, and Ultrareliable/Low-Latency Service. https://app.cochat.ai/papers/10.1109/mvt.2020.3019650
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Du, J., Jiang, C., Wang, J., Ren, Y., & Debbah, M. (2020). Machine Learning for 6G Wireless Networks: Carrying Forward Enhanced Bandwidth, Massive Access, and Ultrareliable/Low-Latency Service. IEEE Vehicular Technology Magazine. https://doi.org/10.1109/mvt.2020.3019650

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