Journal Home Online First Current Issue Archive For Authors Journal Information 中文版

Engineering >> 2022, Volume 8, Issue 1 doi: 10.1016/j.eng.2021.11.003

Toward Wisdom-Evolutionary and Primitive-Concise 6G: A New Paradigm of Semantic Communication Networks

a State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
b Peng Cheng Laboratory, Shenzhen 518066, China
c Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China
d Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China
e National Engineering Laboratory for Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing 100876, China
f School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
g The School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
h College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
i School of Humanities, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received: 2021-01-04 Revised: 2021-05-31 Accepted: 2021-09-01 Available online: 2021-11-17

Next Previous

Abstract

The sixth generation (6G) mobile networks will reshape the world by offering instant, efficient, and intelligent hyper-connectivity, as envisioned by the previously proposed Ubiquitous-X 6G networks. Such hyper-massive and global connectivity will introduce tremendous challenges into the operation and management of 6G networks, calling for revolutionary theories and technological innovations. To this end, we propose a new route to boost network capabilities toward a wisdom-evolutionary and primitive-concise network (WePCN) vision for the Ubiquitous-X 6G network. In particular, we aim to concretize the evolution path toward the WePCN by first conceiving a new semantic representation framework, namely semantic base, and then establishing an intelligent and efficient semantic communication (IE-SC) network architecture. In the IE-SC architecture, a semantic intelligence plane is employed to interconnect the semantic-empowered physical-bearing layer, network protocol layer, and application-intent layer via semantic information flows. The proposed architecture integrates artificial intelligence and network technologies to enable intelligent interactions among various communication objects in 6G. It features a lower bandwidth requirement, less redundancy, and more accurate intent identification. We also present a brief review of recent advances in semantic communications and highlight potential use cases, complemented by a range of open challenges for 6G.

Figures

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

References

[ 1 ] Zhang P, Zhang J, Qi Q, Hu Z, Nie G, Niu K, et al. Ubiquitous-X: constructing the future 6G networks. Sci Sin Inform 2020;50(6):913–30. Chinese. link1

[ 2 ] You X, Wang CX, Huang J, Gao X, Zhang Z, Wang M, et al. Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts. Sci China Inf Sci 2021;64(1):110301. link1

[ 3 ] Shannon CE. A mathematical theory of communication. Bell Syst Tech J 1948;27(3):379–423. link1

[ 4 ] Jiang A, Li Y, Bruck J. Error correction through language processing. In: Proceedings of 2015 IEEE Information Theory Workshop (ITW); 2015 Apr 26– May 1; Jerusalem, Israel; 2015.

[ 5 ] Xie H, Qin Z, Li GY, Juang BH. Deep learning enabled semantic communication systems. IEEE Trans Signal Process 2021;69:2663–75. link1

[ 6 ] Jankowski M, Gündüz D, Mikolajczyk K. Joint device-edge inference over wireless links with pruning. In: Proceedings of 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC); 2020 May 26–29; Atlanta, GA,. USA New York: IEEE; 2020. link1

[ 7 ] Jankowski M, Gündüz D, Mikolajczyk K. Joint device-edge inference over wireless links with pruning. In: Proceedings of 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC); 2020 May 26–29; Atlanta, GA, USA. New York: IEEE; 2020.

[ 8 ] Shi Y, Yao K, Chen H, Pan YC, Hwang MY, Peng B. Contextual spoken language understanding using recurrent neural networks. In: Proceedings of 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2015 Apr 19–24; South Brisbane, QLD, Australia; New York: IEEE; 2015. p. 5271–5

[ 9 ] Morris CW. Foundations of the theory of signs. Chicago: University of Chicago Press; 1938. link1

[10] Weaver W. Recent contributions to the mathematical theory of communication. ETC Rev Gen Semant 1953;10(4):261–81. link1

[11] Carnap R, Bar-Hillel Y. An outline of a theory of semantic information. J Symb Log 1954;19(3):230–2. link1

[12] Bar-Hillel Y, Carnap R. Semantic information. Br J Philos Sci 1953;4 (14):147–57. link1

[13] Barwise J, Perry J. Situations and attitudes. J Philos 1981;78(11):668–91. link1

[14] Floridi L. Outline of a theory of strongly semantic information. Minds Mach 2004;14(2):197–221. link1

[15] D’Alfonso S. On quantifying semantic information. Information 2011;2 (1):61–101. link1

[16] Zhong Y. A theory of semantic information. China Commun 2017;14(1):1–17. link1

[17] Kolchinsky A, Wolpert DH. Semantic information, autonomous agency and non-equilibrium statistical physics. Interface Focus 2018;8(6):20180041. link1

[18] Kountouris M, Pappas N. Semantics-empowered communication for networked intelligent systems. 2020. arXiv:2007.11579.

[19] Rényi A. On measures of entropy and information. In: Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability; 1960 Jun 20–Jul 30; Berkeley, CA, USA. Berkeley: University of California Press; 1960.

[20] Rao M, Farsad N, Goldsmith A. Variable length joint source–channel coding of text using deep neural networks. In: Proceedings of 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC); 2018 Jun 25–28; Kalamata, Greece. New York: IEEE; 2018. p. 1–5.

[21] Guler B, Yener A, Swami A. The semantic communication game. IEEE Trans Cogn Commun Netw 2018;4(4):787–802. link1

[22] Bourtsoulatze E, Burth Kurka D, Gunduz D. Deep joint source-channel coding for wireless image transmission. IEEE Trans Cogn Commun Netw 2019;5 (3):567–79. link1

[23] Weng Z, Qin Z. Semantic communication systems for speech transmission. IEEE J Sel Areas Commun 2021;39(8):2434–44. link1

[24] Alshbatat AI, Dong L. Cross layer design for mobile ad-hoc unmanned aerial vehicle communication networks. In: Proceedings of 2010 International Conference on Networking, Sensing and Control (ICNSC); 2010 Apr 10–12; Chicago, IL, USA. New York: IEEE; p. 331–6.

[25] Awang A, Husain K, Kamel N, Aïssa S. Routing in vehicular ad-hoc networks: a survey on single and cross-layer design techniques, and perspectives. IEEE Access 2017;5:9497–517. link1

[26] Sukhbaatar S, Szlam AD, Fergus R. Learning multiagent communication with backpropagation. In Proceedings of the 30th Annual Conference on Neural Information Processing Systems 2016; 2016 Dec 5–10; Barcelona, Span. New York: Curran Associates Inc.; 2016. p. 2252–60. link1

[27] Wen L, Wang X, Dong Z, Chen H. Jointly modeling intent identification and slot filling with contextual and hierarchical information. In: Proceedings of CCF International Conference on Natural Language Processing and Chinese Computing; 2020 Oct 14–18; Zhengzhou, China. Cham: Springer 2020.

[28] Chao W, Horiuchi S. Intent-based cloud service management. In: Proceedings of 21st Conference on Innovation in Clouds, Internet and Networks and Workshops(ICIN); 2018 Feb 19–22; Paris, France. New York: IEEE; 2018.

[29] Wei Y, Peng M, Liu Y. Intent-based networks for 6G: insights and challenges. Digit Commun Netw 2020;6(3):270–80. link1

[30] Kucˇera H, Francis WN. Computational analysis of present-day American English. Providence: Brown University Press; 1967. link1

[31] Bengio Y, Ducharme R, Vincent P, Jauvin C. A neural probabilistic language model. J Mach Learn Res 2003;3:1137–55. link1

[32] Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space 2013. arXiv:1301.3781v3. link1

[33] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997;9 (8):1735–80. link1

[34] Kingma DP, Ba J. Adam: a method for stochastic optimization. 2014. arXiv: 1412.6980. link1

[35] Zhang R, Isola P, Efros AA, Shechtman E, Wang O. The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2018 Jun 18– 22; Salt Lake City, UT, USA. New York: IEEE; 2018. p. 586–95.

[36] Xie H, Qin Z. A lite distributed semantic communication system for Internet of Things. IEEE J Sel Areas Commun 2021;39(1):142–53. link1

[37] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. In: Proceedings of the 31th International Conference on Neural Information Processing Systems; 2017 Dec 4–9; Long Beach, CA, USA. New York: Curran Associates Inc.; 2017. p. 5998–6008.

[38] Farsad N, Rao M, Goldsmith A. Deep learning for joint source–channel coding of text. In: Proceedings of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2018 Apr 15–20; Calgary, AB, Canada. Stroudsburg: ACL; 2018. p. 2326–30.

[39] Papineni K, Roukos S, Ward T, Zhu WJ. BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics; 2002 Jul 7–12; Stroudsburg, PA, USA; 2002. p. 311–8.

[40] Li L, Zhao Y, Zhang Z, Niu T, Feng F, Wang X. Referring expression generation via visual dialogue. In: Proceedings of the 9th CCF International Conference on Natural Language Processing and Chinese Computing; 2020 Oct 16–18; Zhengzhou, China. Cham: Springer; 2020.

[41] Xu W, Lin J, Feng Z, Xu W, Zhang P. Cognition flow in cognitive radio networks. China Commun 2013;10(10):74–90. link1

[42] Jiang Z, Fu S, Zhou S, Niu Z, Zhang S, Xu S. AI-assisted low information latency wireless networking. IEEE Wirel Commun 2020;27(1):108–15. link1

Related Research