Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 525

The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.

6G networks: Beyond Shannon towards semantic and goal-oriented communications

Open access
Calvanese Strinati, Emilio, Barbarossa, Sergio·2021·Computer Networks
DOI 10.1016/j.comnet.2021.107930
Open this paper in CoChat — free account

Abstract

The goal of this paper is to promote the idea that including semantic and goal-oriented aspects in future 6G networks can produce a significant leap forward in terms of system effectiveness and sustainability.Semantic communication goes beyond the common Shannon paradigm of guaranteeing the correct reception of each single transmitted bit, irrespective of the meaning conveyed by the transmitted bits.The idea is that, whenever communication occurs to convey meaning or to accomplish a goal, what really matters is the impact that the received bits have on the interpretation of the meaning intended by the transmitter or on the accomplishment of a common goal.Focusing on semantic and goal-oriented aspects, and possibly combining them, helps to identify the relevant information, i.e. the information strictly necessary to recover the meaning intended by the transmitter or to accomplish a goal.Combining knowledge representation and reasoning tools with machine learning algorithms paves the way to build semantic learning strategies enabling current machine learning algorithms to achieve better interpretation capabilities and contrast adversarial attacks.6G semantic networks can bring semantic learning mechanisms at the edge of the network and, at the same time, semantic learning can help 6G networks to improve their efficiency and sustainability.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.comnet.2021.107930

Cites

17 of 17 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 17 references

Research works printed but not linked: 43

  1. Calvanese Strinati, 2019, 6G: The next frontier: From holographic messaging to artificial intelligence using subterahertz and visible light communication, IEEE Veh. Technol. Mag., 14, 42, 10.1109/MVT.2019.2921162 · DOI · Paper page
  2. Belot, 2020, Spectrum above 90 GHz for wireless connectivity: Opportunities and challenges for 6G, Microwave J., 63
  3. Popovski, 2020, Semantic-effectiveness filtering and control for post-5G wireless connectivity, J. Indian Inst. Sci., 100, 435, 10.1007/s41745-020-00165-6 · DOI
  4. Letaief, 2019, The roadmap to 6G: Ai empowered wireless networks, IEEE Commun. Mag., 57, 84, 10.1109/MCOM.2019.1900271 · DOI · Paper page
  5. J.-B. Doré, et al. Technology roadmap for beyond 5G wireless connectivity in D-band, in: 2nd 6G Summit, 2020. · doi:10.1109/6gsummit49458.2020.9083890
  6. Zhang, 2019, 6G wireless networks: Vision, requirements, architecture, and key technologies, IEEE Veh. Technol. Mag., 14, 28, 10.1109/MVT.2019.2921208 · DOI · Paper page
  7. Yang, 2019, 6G wireless communications: Vision and potential techniques, IEEE Netw., 33, 70, 10.1109/MNET.2019.1800418 · DOI · Paper page
  8. Calvanese Strinati, 2020, 6G in the sky: on-demand intelligence at the edge of 3D networks, ETRI J., 10.4218/etrij.2020-0205 · DOI
  9. Himesh, 2018, Digital revolution and big data: A new revolution in agriculture, CAB Rev., 27, 1, 10.1079/PAVSNNR201813021 · doi:10.1079/pavsnnr201813021
  10. David, 2018, 6G vision and requirements: Is there any need for beyond 5G?, IEEE Veh. Technol. Mag., 13, 72, 10.1109/MVT.2018.2848498 · DOI · Paper page
  11. Shiroishi, 2018, Society 5.0: For human security and well-being, Computer, 51, 91, 10.1109/MC.2018.3011041 · DOI · Paper page
  12. Giordani, 2020, Toward 6G networks: Use cases and technologies, IEEE Commun. Mag., 58, 55, 10.1109/MCOM.001.1900411 · DOI · Paper page
  13. Tariq, 2020, A speculative study on 6G, IEEE Wirel. Commun., 27, 118, 10.1109/MWC.001.1900488 · DOI · Paper page
  14. R. Li, Towards a new internet for the year 2030 and beyond, in: Proc. 3rd Annu. ITU IMT-2020/5G Workshop Demo Day,2018, pp. 1–21.
  15. Tmt traffic estimates for the years 2020 to 2030, in: Proc. M Series Mobile, radiodetermination, amateur and related satellite services. Report ITU-R M.2370-0, 2015.
  16. H, 2020, Introduction to indoor networking concepts and challenges in lifi, IEEE/OSA J. Opt. Commun. Networking, 12, 190, 10.1364/JOCN.12.00A190 · doi:10.1364/jocn.12.00a190
  17. Liu, 2016, Charging unplugged: Will distributed laser charging for mobile wireless power transfer work?, IEEE Veh. Technol. Mag., 11, 36, 10.1109/MVT.2016.2594944 · DOI
  18. Yaacoub, 2020, A key 6G challenge and opportunity—connecting the base of the pyramid: A survey on rural connectivity, Proc. IEEE, 108, 533, 10.1109/JPROC.2020.2976703 · DOI · Paper page
  19. Berners-Lee, 2001, The semantic web, Sci. Am., 34, 10.1038/scientificamerican0501-34 · DOI · Paper page
  20. Weaver, 1953, Recent contributions to the mathematical theory of communication, ETC: A Review of General Semantics, 261
  21. Floridi, 2002, What is the philosophy of information?, Metaphilosophy, 33, 123, 10.1111/1467-9973.00221 · DOI · Paper page
  22. Chaitin, 1977, Algorithmic information theory, IBM J. Res. Dev., 21, 350, 10.1147/rd.214.0350 · DOI · Paper page
  23. Goldreich, 2012, A theory of goal-oriented communication, J. ACM, 59, 1, 10.1145/2160158.2160161 · DOI
  24. Basu, 2014, Preserving quality of information by using semantic relationships, Pervasive Mob. Comput., 11, 188, 10.1016/j.pmcj.2013.07.013 · DOI
  25. Güler, 2018, The semantic communication game, IEEE Trans. Cogn. Commun. Netw., 4, 787, 10.1109/TCCN.2018.2872596 · DOI
  26. Choi, 2019, Deep frame prediction for video coding, IEEE Trans. Circuits Syst. Video Technol.
  27. Park, 2019, Wireless network intelligence at the edge, Proc. IEEE, 107, 2204, 10.1109/JPROC.2019.2941458 · DOI · Paper page
  28. Skatchkovsky, 2019, Optimizing pipelined computation and communication for latency-constrained edge learning, IEEE Commun. Lett., 23, 1542, 10.1109/LCOMM.2019.2922658 · DOI
  29. Shamir, 2010, Learning and generalization with the information bottleneck, Theoret. Comput. Sci., 411, 2696, 10.1016/j.tcs.2010.04.006 · DOI
  30. N. Helwig, E. Pignanelli, A. Schütze, Condition monitoring of a complex hydraulic system using multivariate statistics, in: 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2015, pp. 210–215. · DOI
  31. Schölkopf, 2000, New support vector algorithms, Neural Comput., 12, 1207, 10.1162/089976600300015565 · DOI
  32. C. Zhang, Q. Cao, H. Jiang, W. Zhang, J. Li, J. Yao, Ffs-va: A fast filtering system for large-scale video analytics, in: Proceedings of the 47th International Conference on Parallel Processing,2018, pp. 1–10. · DOI · Paper page
  33. Letaief, 2019, The roadmap to 6G: Ai empowered wireless networks, IEEE Commun. Mag., 57, 84, 10.1109/MCOM.2019.1900271 · DOI · Paper page
  34. Ndikumana, 2020, Joint communication, computation, caching, and control in big data multi-access edge computing, IEEE Trans. Mob. Comput., 19, 1359, 10.1109/TMC.2019.2908403 · DOI · Paper page
  35. Sardellitti, 2015, Joint optimization of radio and computational resources for multicell mobile-edge computing, IEEE Trans. Signal Inf. Process. Netw., 1, 89
  36. Mao, 2017, Stochastic joint radio and computational resource management for multi-user mobile-edge computing systems, IEEE Trans. Wireless Commun., 16, 5994, 10.1109/TWC.2017.2717986 · DOI · Paper page
  37. Merluzzi, 2020, Dynamic computation offloading in multi-access edge computing via ultra-reliable and low-latency communications, IEEE Trans. Signal Inf. Process. Netw., 6, 342
  38. Chen, 2019, Learning and management for internet of things: Accounting for adaptivity and scalability, Proc. IEEE, 107, 778, 10.1109/JPROC.2019.2896243 · DOI
  39. Barbarossa, 2020, Topological signal processing over simplicial complexes, IEEE Trans. Signal Process., 68, 2992, 10.1109/TSP.2020.2981920 · DOI
  40. Balevi, 2019, One-bit ofdm receivers via deep learning, IEEE Trans. Commun., 67, 4326, 10.1109/TCOMM.2019.2903811 · DOI
  41. Farsad, 2018, Neural network detection of data sequences in communication systems, IEEE Trans. Signal Process., 66, 5663, 10.1109/TSP.2018.2868322 · DOI · Paper page
  42. Dandachi, 2019, An artificial intelligence framework for slice deployment and orchestration in 5g networks, IEEE Transactions on Cognitive Communications and Networking
  43. A. Fawzi, O. Fawzi, P. Frossard, Fundamental limits on adversarial robustness, in: Proc. ICML, Workshop on Deep Learning,2015.

Cited by

See all 525

Metadata

Authors
Calvanese Strinati, E., & Barbarossa, S.
Venue
Computer Networks
Publisher
Elsevier BV
Published
2021-05
Volume
190
Pages
107930
ISSN
1389-1286 (ISSN-L)
DOI
10.1016/j.comnet.2021.107930
OpenAlex
W3107013237
Cited by
525
References
17
FWCI
25.42
Type
journal article
Language
en
Open access
hybrid
Licence

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: 6G networks: Beyond Shannon towards semantic and goal-oriented communications. https://app.cochat.ai/papers/10.1016/j.comnet.2021.107930
The paper
Calvanese Strinati, E., & Barbarossa, S. (2021). 6G networks: Beyond Shannon towards semantic and goal-oriented communications. Computer Networks. https://doi.org/10.1016/j.comnet.2021.107930

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.