The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts
Open accessAbstract
The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Communication Theory of Secrecy Systems
Shannon, C. E. (1949) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1949.tb00928.x · Cited by 9,440
A Survey on Mobile Edge Computing: The Communication Perspective
Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2745201 · Cited by 5,607
A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems
Saad, W., Bennis, M., & Chen, M. (2020) · IEEE Network · doi:10.1109/mnet.001.1900287 · Cited by 4,985
An Introduction to Deep Learning for the Physical Layer
O'Shea, T., & Hoydis, J. (2017) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2017.2758370 · Cited by 2,980
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2986024 · Cited by 2,565
Self-citationNetwork Slicing and Softwarization: A Survey on Principles, Enabling Technologies, and Solutions
Afolabi, I., Taleb, T., Samdanis, K., Ksentini, A., & Flinck, H. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2815638 · Cited by 1,078
Tariq, F., Khandaker, M., Wong, K.-K., Imran, M., Bennis, M., & Debbah, M. (2019) · arXiv · doi:10.48550/arxiv.1902.06700 · Cited by 1,014
Deep Learning Based Communication Over the Air
Dorner, S., Cammerer, S., Hoydis, J., & Brink, S. T. (2018) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2017.2784180 · Cited by 859
Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems
He, H., Wen, C.-K., Jin, S., & Li, G. Y. (2018) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2018.2832128 · Cited by 823
Self-citation
Cited by
6G Internet of Things: A Comprehensive Survey
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., Niyato, D., Dobre, O., & Poor, H. V. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3103320 · Cited by 1,452
Self-citationNoor-A-Rahim, M., Liu, Z., Lee, H., Khyam, M. O., He, J., Pesch, D., Moessner, K., Saad, W., & Poor, H. V. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3173031 · Cited by 737
Self-citationCellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Toward 6G
Vaezi, M., Azari, A., Khosravirad, S. R., Shirvanimoghaddam, M., Azari, M. M., Chasaki, D., & Popovski, P. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3151028 · Cited by 631
A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G
Zeng, Y., Chen, J., Xu, J., Wu, D., Xu, X., Jin, S., Gao, X., Gesbert, D., Cui, S., & Zhang, R. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3364508 · Cited by 303
Self-citationImoize, A. L., Adedeji, O., Tandiya, N., & Shetty, S. (2021) · Sensors · doi:10.3390/s21051709 · Cited by 281
Metadata
- Authors
- You, X., Wang, C.-X., Huang, J., Gao, X., Zhang, Z., Wang, M., Huang, Y., Zhang, C., Jiang, Y., Wang, J., Zhu, M., Sheng, B., Wang, D., Pan, Z., Zhu, P., Yang, Y., Liu, Z., Zhang, P., Tao, X., Li, S., Chen, Z., Ma, X., I, C.-L., Han, S., Li, K., Pan, C., Zheng, Z., Hanzo, L., Shen, X. (., Guo, Y. J., Ding, Z., Haas, H., Tong, W., Zhu, P., Yang, G., Wang, J., Larsson, E. G., Ngo, H. Q., Hong, W., Wang, H., Hou, D., Chen, J., Chen, Z., Hao, Z., Li, G. Y., Tafazolli, R., Gao, Y., Poor, H. V., Fettweis, G. P., & Liang, Y.-C.
- Venue
- Science China Information Sciences
- Published
- 2020-11-24
- Volume
- 64
- Issue
- 1
- ISSN
- 1869-1919 (ISSN-L)
- 1674-733X
- OpenAlex
- W3110989801
- Cited by
- 2,027
- References
- 31
- FWCI
- 89.88
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts. https://app.cochat.ai/papers/10.1007/s11432-020-2955-6
- The paper
- You, X., Wang, C.-X., Huang, J., Gao, X., Zhang, Z., Wang, M., Huang, Y., Zhang, C., Jiang, Y., Wang, J., Zhu, M., Sheng, B., Wang, D., Pan, Z., Zhu, P., Yang, Y., Liu, Z., Zhang, P., Tao, X., . . . Liang, Y.-C. (2021). Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts. Science China Information Sciences. https://doi.org/10.1007/s11432-020-2955-6
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.