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

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.

• Record URL:
  http://dx.doi.org/10.1109/MVT.2020.3019650
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/15566072
• Supplemental Notes:
  • Copyright © 2020, IEEE.
• Authors:
  • Du, Jun
  • Jiang, Chunxiao
  • Wang, Jian
  • Ren, Yong
  • Debbah, Merouane
• Publication Date: 2020-12

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 122-134
• Serial:
  • IEEE Vehicular Technology Magazine
  • Volume: 15
  • Issue Number: 4
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1556-6072
  • Serial URL: http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=34840

Subject/Index Terms

• TRT Terms: Access; Artificial intelligence; Bandwidth; Machine learning; Mobility; Quality of service; State of the art; Wireless communication systems
• Subject Areas: Data and Information Technology; Transportation (General); Vehicles and Equipment;

Filing Info

• Accession Number: 01761171
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 23 2020 10:03AM