- Title
- 6G wireless systems : a vision, architectural elements, and future directions
- Creator
- Khan, Latif; Yaqoob, Ibrar; Imran, Muhammad; Han, Zhu; Hong, Choong
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/184170
- Identifier
- vital:16441
- Identifier
-
https://doi.org/10.1109/ACCESS.2020.3015289
- Identifier
- ISBN:2169-3536 (ISSN)
- Abstract
- Internet of everything (IoE)-based smart services are expected to gain immense popularity in the future, which raises the need for next-generation wireless networks. Although fifth-generation (5G) networks can support various IoE services, they might not be able to completely fulfill the requirements of novel applications. Sixth-generation (6G) wireless systems are envisioned to overcome 5G network limitations. In this article, we explore recent advances made toward enabling 6G systems. We devise a taxonomy based on key enabling technologies, use cases, emerging machine learning schemes, communication technologies, networking technologies, and computing technologies. Furthermore, we identify and discuss open research challenges, such as artificial-intelligence-based adaptive transceivers, intelligent wireless energy harvesting, decentralized and secure business models, intelligent cell-less architecture, and distributed security models. We propose practical guidelines including deep Q-learning and federated learning-based transceivers, blockchain-based secure business models, homomorphic encryption, and distributed-ledger-based authentication schemes to cope with these challenges. Finally, we outline and recommend several future directions. © 2013 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Access Vol. 8, no. (2020), p. 147029-147044
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright @ IEEE
- Rights
- Open Access
- Subject
- 40 Engineering; 46 Information and Computing Sciences; 5G; 6G; Blockchain; Federated learning; Internet of Everything; Internet of Things; Meta learning
- Full Text
- Reviewed
- Funder
- This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant funded by the Korean Government Ministry of Science and ICT (MSIT), South Korea, Evolvable Deep Learning Model Generation Platform for Edge Computing, under Grant 2019-0-01287, and in part by MSIT through the Grand Information Technology Research Center Support Program, supervised by the Institute for Information and Communications Technology Planning and Evaluation (IITP), under Grant IITP-2020-2015-0-00742.
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