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Semantic-Effectiveness Filtering and Control for Post-5G Wireless Connectivity

Abstract

The traditional role of a communication engineer is to address the technical problem of transporting bits reliably over a noisy channel. With the emergence of 5G, and the availability of a variety of competing and coexisting wireless systems, wireless connectivity is becoming a commodity. This article argues that communication engineers in the post-5G era should extend the scope of their activity in terms of design objectives and constraints beyond connectivity to encompass the semantics of the transferred bits within the given applications and use cases. To provide a platform for semantic-aware connectivity solutions, this paper introduces the concept of a semantic-effectiveness (SE) plane as a core part of future communication architectures. The SE plane augments the protocol stack by providing standardized interfaces that enable information filtering and direct control of functionalities at all layers of the protocol stack. The advantages of the SE plane are described in the perspective of recent developments in 5G, and illustrated through a number of example applications. The introduction of a SE plane may help replacing the current “next-G paradigm” in wireless evolution with a framework based on continuous improvements and extensions of the systems and standards.

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References

  1. 1.

    Shannon CE, Weaver W (1964) The mathematical theory of communication. University of Illinois Press, Champaign

    Google Scholar 

  2. 2.

    Clark DD, Partridge C, Ramming JC, Wroclawski JT (2003) A knowledge plane for the internet. In: Proceedings of the 2003 conference on applications, technologies, architectures, and protocols for computer communications, ser. SIGCOMM ’03. ACM, New York, pp 3–10. https://doi.org/10.1145/863955.863957

  3. 3.

    Sooriyabandara M, Farnham T, Mahonen P, Petrova M, Riihijarvi J, Wang Z (2011) Generic interface architecture supporting cognitive resource management in future wireless networks. IEEE Commun Mag 49(9):103–113

    Article  Google Scholar 

  4. 4.

    Simeone O (2018) A very brief introduction to machine learning with applications to communication systems. IEEE Trans Cogn Commun Netw 4(4):648–664

    Article  Google Scholar 

  5. 5.

    Altman E, Azouzi RE, Menasché DS, Xu Y (2010) Forever young: Aging control in DTNs. CoRR. arxiv:abs/1009.4733 . arXiv:1009.4733

  6. 6.

    3GPP (2019) Architecture enhancements for 5G System (5GS) to support network data analytics services; Stage 2 (Release 16). In: 3rd generation partnership project (3GPP), technical specification (TS) 23.288, 2, version 0.1.0

  7. 7.

    Christidis K, Devetsikiotis M (2016) Blockchains and smart contracts for the internet of things. IEEE Access 4:2292–2303

    Article  Google Scholar 

  8. 8.

    Popovski P, Trillingsgaard KF, Simeone O, Durisi G (2018) 5g wireless network slicing for embb, urllc, and mmtc: a communication-theoretic view. IEEE Access 6:55 765–55 779

    Article  Google Scholar 

  9. 9.

    Petrov V, Fodor G, Kokkoniemi J, Moltchanov D, Lehtomaki J, Andreev S, Koucheryavy Y, Juntti M, Valkama M (2019) On unified vehicular communications and radar sensing in millimeter-wave and low terahertz bands. arXiv e-prints, arXiv:1901.06980

  10. 10.

    Popovski P, Simeone O (2010) Protocol coding for two-way communications with half-duplex constraints. In: 2010 IEEE global telecommunications conference GLOBECOM 2010. IEEE, pp 1–5

  11. 11.

    Amiri MM, Gunduz D (2019) Machine learning at the wireless edge: distributed stochastic gradient descent over-the-air. arXiv e-prints. arXiv:1901.00844

  12. 12.

    Konecný J, McMahan HB, Ramage D, Richtárik P (2016) Federated optimization: distributed machine learning for on-device intelligence. CoRR. arxiv:1610.02527. arXiv:1610.02527

  13. 13.

    Xu Q, Zheng R, Saad W, Han Z (2016) Device fingerprinting in wireless networks: challenges and opportunities. IEEE Commun Surv Tutor 18(1):94–104

    Article  Google Scholar 

  14. 14.

    Roush W, Pontin M (2018) Twelve tomorrows. MIT Press, Cambridge

    Book  Google Scholar 

  15. 15.

    Zhu G, Liu D, Du Y, You C, Zhang J, Huang K (2018) Towards an intelligent edge: wireless communication meets machine learning. arXiv preprint. arXiv:1809.00343

  16. 16.

    3GPP (2018) System architecture for the 5G system; Stage 2 (Release 15). 3rd Generation Partnership Project (3GPP), Technical Specification (TS) 23.501, 12 2018, version 15.4

  17. 17.

    3GPP (2019) Study on RAN-centric data collection and utilization for LTE and NR (Release 16). In: 3rd Generation Partnership Project (3GPP), Technical Report (TR) 37.816, 3 2019, version 0.2.0

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Acknowledgements

This work has received funding from the European Research Council (ERC) under the European Union Horizon 2020 research and innovation program (Grant agreements 725731, 677854 and 648382).

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Correspondence to Petar Popovski.

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Popovski, P., Simeone, O., Boccardi, F. et al. Semantic-Effectiveness Filtering and Control for Post-5G Wireless Connectivity. J Indian Inst Sci 100, 435–443 (2020). https://doi.org/10.1007/s41745-020-00165-6

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