The DOI matches its Crossref record; 12 of the 15 references it cites were found in a registry or an index.
A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems
Open accessAbstract
The ongoing deployment of 5G cellular systems is continuously exposing the inherent limitations of this system, compared to its original premise as an enabler for Internet of Everything applications. These 5G drawbacks are spurring worldwide activities focused on defining the next-generation 6G wireless system that can truly integrate far-reaching applications ranging from autonomous systems to extended reality. Despite recent 6G initiatives (one example is the 6Genesis project in Finland), the fundamental architectural and performance components of 6G remain largely undefined. In this article, we present a holistic, forward-looking vision that defines the tenets of a 6G system. We opine that 6G will not be a mere exploration of more spectrum at high-frequency bands, but it will rather be a convergence of upcoming technological trends driven by exciting, underlying services. In this regard, we first identify the primary drivers of 6G systems, in terms of applications and accompanying technological trends. Then, we propose a new set of service classes and expose their target 6G performance requirements. We then identify the enabling technologies for the introduced 6G services and outline a comprehensive research agenda that leverages those technologies. We conclude by providing concrete recommendations for the roadmap toward 6G. Ultimately, the intent of this article is to serve as a basis for stimulating more out-of-the-box research around 6G.
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“AI-driven healthcare improves clinical diagnosis and decision making [35].”
From 6G Communication Technology: A Vision on Intelligent Healthcare (2021) · cites this paper as [35]
Citations
Cites
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial
Chen, M., Challita, U., Saad, W., Yin, C., & Debbah, M. A. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2926625 · Cited by 1,378
Self-citationToward Massive, Ultrareliable, and Low-Latency Wireless Communication With Short Packets
Durisi, G., Koch, T., & Popovski, P. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2016.2537298 · Cited by 1,182
Beyond Massive MIMO: The Potential of Data Transmission With Large Intelligent Surfaces
Hu, S., Rusek, F., & Edfors, O. (2018) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2018.2816577 · Cited by 892
Chen, M., Saad, W., & Yin, C. (2018) · IEEE Transactions on Communications · doi:10.1109/tcomm.2018.2850303 · Cited by 289
Self-citationIntelligent Offloading in Multi-Access Edge Computing: A State-of-the-Art Review and Framework
Cao, B., Zhang, L., Li, Y., Feng, D., & Cao, W. (2019) · IEEE Communications Magazine · doi:10.1109/mcom.2019.1800608 · Cited by 274
Propagation Measurement System and Approach at 140 GHz-Moving to 6G and Above 100 GHz
Xing, Y., & Rappaport, T. S. (2018) · Globecom · doi:10.1109/glocom.2018.8647921 · Cited by 252
Semiari, O., Saad, W., Bennis, M., & Debbah, M. A. (2019) · IEEE Wireless Communications · doi:10.1109/mwc.2019.1800039 · Cited by 138
Self-citationLi, Y., Liu, J., Cao, B., & Wang, C. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2796246 · Cited by 119
Human-in-the-Loop Wireless Communications: Machine Learning and Brain-Aware Resource Management
Kasgari, A. T. Z., Saad, W., & Debbah, M. A. (2019) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2930275 · Cited by 47
Self-citationModel-Free Ultra Reliable Low Latency Communication (URLLC): A Deep Reinforcement Learning Framework
Kasgari, A. T. Z., & Saad, W. (2019) · International Conference on Communications · doi:10.1109/icc.2019.8761721 · Cited by 45
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Federated Deep Learning for Immersive Virtual Reality over Wireless Networks
Chen, M., Semiari, O., Saad, W., Liu, X., & Yin, C. (2019) · 2019 IEEE Global Communications Conference (GLOBECOM) · doi:10.1109/globecom38437.2019.9013419 · Cited by 19
Self-citationReliable and Efficient Data Collection in UAV-based IoT Networks
Joshi, P., Kalita, A., & Gurusamy, M. (2023) · arXiv · doi:10.48550/arxiv.2311.05303 · Cited by 61
“Restrictions apply. collecting sensing data from surrounding sensors and UAVs and aggregating the data for further processing.”
Metadata
- Authors
- Saad, W., Bennis, M., & Chen, M.
- Affiliations
- Virginia Tech (US)
- University of Oulu (FI)
- Princeton University (US)
- Venue
- IEEE Network
- Published
- 2020-05
- Volume
- 34
- Issue
- 3
- Pages
- 134-142
- ISSN
- 0890-8044 (ISSN-L)
- 1558-156X
- OpenAlex
- W2981096252
- Cited by
- 4,985
- References
- 15
- Citations by year
- 2026: 676 · 2025: 830 · 2024: 827 · 2023: 826 · 2022: 723 · 2021: 709 · 2020: 324
- FWCI
- 163.74
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Network (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1836802)
- European Commission (101017109)
- Ministerio de Economía y Competitividad
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Technologies
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Millimeter-Wave Propagation and Modeling
- Keywords
- 6G, 5G limitations, Internet of Everything, service classes, performance requirements, enabling technologies, autonomous systems, extended reality, high-frequency spectrum
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