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迈向6G智简网络——基于语义通信的网络新范式
Ping Zhang,
Wenjun Xu,
Hui Gao,
Kai Niu,
Xiaodong Xu,
Xiaoqi Qin,
Caixia Yuan,
Zhijin Qin,
Haitao Zhao,
Jibo Wei,
Fangwei Zhang
工程（英文） ›› 2022, Vol. 8 ›› Issue (1) : 60-73.
PDF(3651 KB)
PDF(3651 KB)
迈向6G智简网络——基于语义通信的网络新范式
Toward Wisdom-Evolutionary and Primitive-Concise 6G: A New Paradigm of Semantic Communication Networks
第六代（6G）移动网络将通过“以实创虚、基虚利实”新愿景实现虚实交融、世界重塑，该愿景已在我们前期工作提出的Ubiquitous-X 6G网络中进行构思并呈现。6G网络超大规模的全局性连接将给网络的运营和管理带来巨大挑战，亟待革命性的理论和技术创新。为此，本文提出了推动Ubiquitous-X 6G网络迈向“智慧演化和原生简约”，即“智简”网络（wisdom-evolutionary and primitive-concise network, WePCN）的新途径——以深入挖掘信息的语义层次内涵为主线，首先提出了全新的语义表征框架模型，即语义基（semantic base），进而构建了面向“智简”6G的“一面-三层”智能高效语义通信（intelligent and efficient semantic communication, IE-SC）网络架构。IE-SC网络架构通过语义智能平面以及基于语义基表征的语义信息流，将语义赋能的物理承载层、网络协议层和应用意图层相互连接，使网络具备更低的带宽需求、更低的冗余度、更准确的通信意图识别等能力。IE-SC网络架构赋能人工智能和通信网络技术的一体化，实现6G网络中多种通信对象间的智能信息交互。此外，本文还简要介绍了语义通信的新进展，指出了语义通信的潜在应用方向、开放性问题与挑战。
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.
第六代（6G）移动通信 / 语义信息 / 语义通信 / 智能通信
6G / Semantic information / Semantic communication / Intelligent communication
| [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.
|
| [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.
|
| [3] |
Shannon CE. A mathematical theory of communication. Bell Syst Tech J 1948;27(3):379–423.
|
| [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.
|
| [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.
|
| [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.
|
| [10] |
Weaver W. Recent contributions to the mathematical theory of communication. ETC Rev Gen Semant 1953;10(4):261–81.
|
| [11] |
Carnap R, Bar-Hillel Y. An outline of a theory of semantic information. J Symb Log 1954;19(3):230–2.
|
| [12] |
Bar-Hillel Y, Carnap R. Semantic information. Br J Philos Sci 1953;4 (14):147–57.
|
| [13] |
Barwise J, Perry J. Situations and attitudes. J Philos 1981;78(11):668–91.
|
| [14] |
Floridi L. Outline of a theory of strongly semantic information. Minds Mach 2004;14(2):197–221.
|
| [15] |
D’Alfonso S. On quantifying semantic information. Information 2011;2 (1):61–101.
|
| [16] |
Zhong Y. A theory of semantic information. China Commun 2017;14(1):1–17.
|
| [17] |
Kolchinsky A, Wolpert DH. Semantic information, autonomous agency and non-equilibrium statistical physics. Interface Focus 2018;8(6):20180041.
|
| [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.
|
| [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.
|
| [23] |
Weng Z, Qin Z. Semantic communication systems for speech transmission. IEEE J Sel Areas Commun 2021;39(8):2434–44.
|
| [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.
|
| [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.
|
| [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.
|
| [30] |
Kucˇera H, Francis WN. Computational analysis of present-day American English. Providence: Brown University Press; 1967.
|
| [31] |
Bengio Y, Ducharme R, Vincent P, Jauvin C. A neural probabilistic language model. J Mach Learn Res 2003;3:1137–55.
|
| [32] |
Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space 2013. arXiv:1301.3781v3.
|
| [33] |
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997;9 (8):1735–80.
|
| [34] |
Kingma DP, Ba J. Adam: a method for stochastic optimization. 2014. arXiv: 1412.6980.
|
| [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.
|
| [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.
|
| [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.
|
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| 〈 |
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