The DOI matches its Crossref record; 55 of the 58 references it cites were found in a registry or an index.
6G: Connecting Everything by 1000 Times Price Reduction
Open accessAbstract
The commercial deployment of 5G communication networks makes the industry and academia to seriously consider the possible solutions for the next generation. Although the conventional approach indicates that 6G vision and requirements can be figured out by simply multiplying a certain amount of magnitude on top of the previous generations, we argue in this article that 1000 times price reduction from the customer's view point is the key to success. Guided by this vision, we categorize the current candidate technologies in a well organized manner and select AI-assisted intelligent communications as an example to elaborate the drive-force behind. Although it is impossible to identify every detail of 6G during the current time frame, we believe this article will help to eliminate the technical uncertainties and aggregate the efforts towards key breakthrough innovations for 6G.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Also, it requires AI to provide personalized healthcare [46].”
From 6G Communication Technology: A Vision on Intelligent Healthcare (2021) · cites this paper as [46]
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
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Enhanced Deep Residual Networks for Single Image Super-Resolution
Lim, B. O., Son, S., Kim, H., Nah, S., & Lee, K. M. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.151 · Cited by 8,117
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Kim, J., Lee, J. K., & Lee, K. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.182 · Cited by 7,870 · cited 2×
“To further improve the channel matrix recovery NMSE, deep neural networks based technologies, such as the super-resolution convolutional neural network (SRCNN) [41], the very deep super-resolution network (VDSR) [42] and enhanced deep super-resolution (EDSR) [43] have been applied into channel state interpolation task…”
MethodImage Super-Resolution Via Sparse Representation
Yang, J., Wright, J. N., Huang, T. S., & Ma, Y. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2050625 · Cited by 5,241
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
Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication
Huang, C., Zappone, A., Alexandropoulos, G. C., Debbah, M. A., & Yuen, C. (2019) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2922609 · Cited by 3,774
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2018) · Science · doi:10.1126/science.aar6404 · Cited by 3,562 · cited 1×
“deep reinforcement learning [24], has demonstrated their potential to deal with huge scale optimization problems, such as playing chess or Go games, and the famous AlphaZero can beat top Go player even without the prior human experts’ knowledge [25].”
Background6G Wireless Networks: Vision, Requirements, Architecture, and Key Technologies
Zhang, Z., Xiao, Y., Ma, Z., Xiao, M., Ding, Z., Lei, X., Karagiannidis, G. K., & Fan, P. (2019) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2019.2921208 · Cited by 2,575
Example-based super-resolution
Freeman, W. T., Jones, T. R., & Pasztor, E. C. (2002) · IEEE Computer Graphics and Applications · doi:10.1109/38.988747 · Cited by 2,497
The Roadmap to 6G: AI Empowered Wireless Networks
Letaief, K. B., Chen, W., Shi, Y., Zhang, J., & Zhang, Y.-J. A. (2019) · IEEE Communications Magazine · doi:10.1109/mcom.2019.1900271 · Cited by 2,207
Research works printed but not linked: 3
- saad, 0, A vision of 6G wireless systems: Applications, trends, technologies, and open research problems, IEEE Netw
- 10.1109/MCOM.2019.1900271 · DOI · Paper page
- shi, 0, Real-time single image and video super-resolution using an efficient bub-pixel convolutional neural network, Proc IEEE Conf Comput Vision Pattern Recognit, 1874
Cited by
Sorted by citations
Chowdhury, M. Z., Shahjalal, M. A., Ahmed, S., & Jang, Y. M. (2020) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2020.3010270 · Cited by 1,925
The Road Towards 6G: A Comprehensive Survey
Jiang, W., Han, B., Habibi, M. A., & Schotten, H. D. (2021) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2021.3057679 · Cited by 1,757
6G Wireless Communications Networks: A Comprehensive Survey
Alsabah, M. Q., Naser, M. A., Mahmmod, B. M., Abdulhussain, S. H., Eissa, M. R., Al-Baidhani, A., Noordin, N. K., Sait, S. M., Al-Utaibi, K. A., & Hashim, F. (2021) · IEEE Access · doi:10.1109/access.2021.3124812 · Cited by 622
A Prospective Look: Key Enabling Technologies, Applications and Open Research Topics in 6G Networks
Bariah, L., Mohjazi, L., Muhaidat, S., Sofotasios, P. C., Kurt, G. K., Yanıkömeroğlu, H., & Dobre, O. A. (2020) · IEEE Access · doi:10.1109/access.2020.3019590 · Cited by 356
6G: A survey on technologies, scenarios, challenges, and the related issues
Lu, Y., & Zheng, X. (2020) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2020.100158 · Cited by 360
Metadata
- Authors
- Zhang, S., Xiang, C., & Xu, S.
- Affiliations
- Shanghai University (CN)
- Venue
- IEEE Open Journal of Vehicular Technology
- Published
- 2020
- Volume
- 1
- Pages
- 107-115
- ISSN
- 2644-1330 (ISSN-L)
- OpenAlex
- W3011296336
- Cited by
- 105
- References
- 58
- Citations by year
- 2026: 5 · 2025: 18 · 2024: 14 · 2023: 20 · 2022: 15 · 2021: 22 · 2020: 11
- FWCI
- 5.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Open Journal of Vehicular Technology (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61701293, 61871262)
- National Science and Technology Major Project (2018ZX03001009)
- National Key Research and Development Program of China (2017YFE0121400)
- Huawei Innovation Research Program
- Shanghai Institute for Advanced Communication and Data Science
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Data and IoT Technologies
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- 6G, next-generation wireless networks, cost reduction, customer perspective, network requirements, technology categorization
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
