Intellicise communication system: model-driven semantic
communications

doi:10.19682/j.cnki.1005-8885.2022.2002

中国邮电高校学报(英文) ›› 2022, Vol. 29 ›› Issue (1): 2-12.doi: 10.19682/j.cnki.1005-8885.2022.2002

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Intellicise communication system: model-driven semantic communications

Zhang Ping, Xu Xiaodong, Dong Chen, Han Shujun, Wang Bizhu

State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

收稿日期:2022-01-17 修回日期:2022-01-20 接受日期:2022-01-22 出版日期:2022-02-26 发布日期:2022-02-28
通讯作者: Corresponding author: Xu Xiaodong E-mail:xuxiaodong@bupt.edu.cn
基金资助:
This work was supported by the National Natural Science Foundation of China (61871045).

Intellicise communication system: model-driven semantic communications

Zhang Ping, Xu Xiaodong, Dong Chen, Han Shujun, Wang Bizhu

State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2022-01-17 Revised:2022-01-20 Accepted:2022-01-22 Online:2022-02-26 Published:2022-02-28
Contact: Corresponding author: Xu Xiaodong E-mail:xuxiaodong@bupt.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (61871045).

摘要/Abstract

摘要：

As one of the critical technologies for the 6th generation mobile communication system (6G) mobile communication systems, artificial intelligence (AI) technology will provide complete automation for connecting the virtual and physical worlds. In order to construct the future ubiquitous intelligent network, people are beginning to rethink how mobile communication systems transmit and exploit intelligent information. This paper proposes a new communication paradigm, called the Intellicise communication system: model-driven semantic communication. Intellicise communication system is built on top of the traditional communication system and innovatively adds a new feature dimension on top of the traditional source coding, which enables the communication system to evolve from the traditional transmission of bit to the transmission of “model”. Like the semantic base (Seb) for semantic communication, the model is considered as the new feature obtained from the joint source-channel coding. The sink node can re-construct the original signal based on the received model and the encoded sequence. In addition, the performance evaluation metrics and the implementation details of the Intellicise communication system are discussed in this paper. Finally, preliminary results of model-driven image transmission in the Intellicise communication system are presented.

关键词: Intellicise communication system, semantic communications, model driven, the 6th generation mobile communication system, artificial intelligence

Abstract: As one of the critical technologies for the 6th generation mobile communication system (6G) mobile communication systems, artificial intelligence (AI) technology will provide complete automation for connecting the virtual and physical worlds. In order to construct the future ubiquitous intelligent network, people are beginning to rethink how mobile communication systems transmit and exploit intelligent information. This paper proposes a new communication paradigm, called the Intellicise communication system: model-driven semantic communication. Intellicise communication system is built on top of the traditional communication system and innovatively adds a new feature dimension on top of the traditional source coding, which enables the communication system to evolve from the traditional transmission of bit to the transmission of “model”. Like the semantic base (Seb) for semantic communication, the model is considered as the new feature obtained from the joint source-channel coding. The sink node can re-construct the original signal based on the received model and the encoded sequence. In addition, the performance evaluation metrics and the implementation details of the Intellicise communication system are discussed in this paper. Finally, preliminary results of model-driven image transmission in the Intellicise communication system are presented.

Key words: Intellicise communication system, semantic communications, model driven, the 6th generation mobile communication system, artificial intelligence

中图分类号:

TN929.5

Zhang Ping, Xu Xiaodong, Dong Chen, Han Shujun, Wang Bizhu. Intellicise communication system: model-driven semantic communications[J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(1): 2-12.

参考文献

References

[1] LATVA-AHO M, LEPPANEN K, CLAZZER F, et al. Key drivers and research challenges for 6G ubiquitous wireless intelligence. Oulu, Finland: 6G Flagship, University of Oulu, 2019.

[2] LI R. Network 2030-A blueprint of technology, applications and market drivers towards the year 2030 and beyond. ITU-T Technical Report, FG NET 2030. 2019.

[3] 6G: The next hyper-connected experience for all. White Paper. Samsung Research, 2020.

[4] 5G evolution and 6G. White Paper. Tokyo, Japan: NTT Docomo Inc, 2020.

[5] Report of vision and requirements for 2030 + . Beijing, China: China Mobile Research Institute, 2019.

[6] DAVID K, BERNDT H. 6G vision and requirements: Is there any need for beyond 5G? IEEE Vehicular Technology Magazine, 2018, 13(3): 72 - 80.

[7] ZHANG Z Q, XIAO Y, MA Z, et al. 6G wireless networks: Vision, requirements, architecture, and key technologies. IEEE Vehicular Technology Magazine, 2019, 14(3): 28 - 41.

[8] ZHANG P, ZHANG J H, QI Q, et al. Ubiquitous-X: Constructing the future 6G networks. Scientia Sinica: Informationis, 2020, 50(6): 913 - 930 (in Chinese).

[9] DANG S, AMIN O, SHIHADA B, et al. What should 6G be? Nature Electronics, 2020, 3(1): 20 - 29.

[10] YOU X, WANG C X, HUANG J, et al. Towards 6G wireless communication networks: Vision, enabling technologies, and new paradigm shifts. Science China: Information Sciences, 2021, 64: Article 110301.

[11] ZHANG P, LI S L, HU Z. Enhanced-mobile ubiquitous smart environment-The super state of Internet of things. Chinese Journal on Internet of Things, 2018, 2(1): 17 - 23.

[12] ELSAYED M, EROL-KANTARCI M. AI-enabled future wireless networks: Challenges, opportunities, and open issues. IEEE Vehicular Technology Magazine, 2019, 14(3): 70 - 77.

[13] OUYANG Y, WANG L L, YANG A D, et al. The next decade of telecommunications artificial intelligence. ArXiv: 2101. 09163, 2021.

[14] ZHANG P, XU W J, GAO H, et al. Toward wisdom-evolutionary and primitive-concise 6G: A new paradigm of semantic communication networks. Engineering, 2022, 8(1): 60 - 73.

[15] ZHANG P, NIU K, TIAN H, et al. Technology prospect of 6G mobile communications. Journal on Communications, 2019, 40(1): 141 - 148 (in Chinese).

[16] SHANNON C E. A mathematical theory of communication. The Bell System Technical Journal, 1948, 27(3): 379 - 423.

[17] WEAVER W. Recent contributions to the mathematical theory of communication. ETC: A Review of General Semantics, 1953, 10(4): 261 - 281.

[18] STRINATI E C, BARBAROSSA S. 6G networks: Beyond Shannon towards semantic and goal-oriented communications. Computer Networks, 2021, 190: Article 107930.

[19] LU Y Q, ASGHAR M R. Semantic communications between distributed cyber-physical systems towards collaborative automation for smart manufacturing. Journal of Manufacturing Systems, 2020, 55: 348 - 359.

[20] KALFA M, GOK M, ATALIK A, et al. Towards goal-oriented semantic signal processing: Applications and future challenges. Digital Signal Processing, 2021, 119: Article 103134.

[21] GULER B, YENER A, SWAMI A. The semantic communication game. IEEE Transactions on Cognitive Communications and Networking, 2018, 4(4): 787 - 802.

[22] JABBAR S, ULLAH F, KHALID S, et al. Semantic interoperability in heterogeneous IoT infrastructure for healthcare. Wireless Communications and Mobile Computing, 2017: Article 9731806.

[23] RAHMAN M A, HOSSAIN M S, HASSANAIN E, et al. Semantic multimedia fog computing and IoT environment: Sustainability perspective. IEEE Communications Magazine, 2018, 56(5): 80 - 87.

[24] XIE H Q, QIN Z J. A lite distributed semantic communication system for Internet of things. IEEE Journal on Selected Areas in Communications, 2021, 39(1): 142 - 153.

[25] SHI G M, XIAO Y, LI Y Y, et al. From semantic communication to semantic-aware networking: Model, architecture, and open problems. IEEE Communications Magazine, 2021, 59(8): 44 - 50.

[26] KOUNTOURIS M, PAPPAS N. Semantics-empowered communication for networked intelligent systems. IEEE Communications Magazine, 2021, 59(6): 96 - 102.

[27] FARSHBAFAN M K, SAAD W, DEBBAH M. Common language for goal-oriented semantic communications: A curriculum learning framework. ArXiv: 2111. 08051, 2021.

[28] XIE H Q, QIN Z J, LI G Y, et al. Deep learning enabled semantic communication systems. IEEE Transactions on Signal Processing, 2021, 69: 2663 - 2675.

[29] WENG Z Z, QIN Z J. Semantic communication systems for speech transmission. IEEE Journal on Selected Areas in Communications, 2021, 39(8): 2434 - 2444.

[30] LAN Q, WEN D Z, ZHANG Z Z, et al. What is semantic communication? A view on conveying meaning in the era of machine intelligence. Journal of Communications and Information Networks, 2021, 6(4): 336 - 371.

[31] LU K, ZHOU Q Y, LI R P, et al. Rethinking modern communication from semantic coding to semantic communication. ArXiv: 2110. 08496, 2021.

[32] WENG Z Z, QIN Z J, LI G Y. Semantic communications for speech signals. Proceeding of the 2021 IEEE International Conference on Communications (ICC'21), 2021, Jun 14 - 23, Montreal, Canada. Piscataway, NJ, USA: IEEE, 2021: 1 - 6.

[33] GUO J F, FAN Y X, AI Q Y, et al. A deep relevance matching model for Ad-hoc retrieval. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (CIKM'16), 2016, Oct 24 - 28, Indianapolis, IN, USA. New York, NY, USA: ACM, 2016: 55 - 64.

[34] ZAMANI H, DEHGHANI M, CROFT W B, et al. From neural re-ranking to neural ranking: Learning a sparse representation for inverted indexing. Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM'18), 2018, Oct 22 - 26, Torino, Italy. New York, NY, USA: ACM, 2018: 497 - 506.

[35] MACAVANEY S, YATES A, COHAN A, et al. CEDR: Contextualized embeddings for document ranking. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'19), 2019, Jul 21 - 25, Paris, France. New York, NY, USA: ACM, 2019: 1101 - 1104.

[36] PAPINENI K, ROUKOS S, WARD T, et al. BLEU: A method for automatic evaluation of machine translation. Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, 2002, Jul 7 - 12, Philadelphia, PA, USA. Stroudsburg, PA, USA: Association for Computational Linguistics, 2002: 311 - 318.

[37] MALIK A A, HABIB A. Urdu to English machine translation using bilingual evaluation understudy. International Journal of Computer Applications, 2013, 82(7): 5 - 12.

[38] PRZYBOCKI M A, MARTIN A F. The 1999 NIST speaker recognition evaluation, using summed two-channel telephone data for speaker detection and speaker tracking. Proceeding of the 6th European Conference on Speech Communication and Technology, 1999, Sept 5 - 10, Budapest, Hungary. 1999: 799 - 802.

[39] KIM S N, BALDWIN T, KAN M Y. Evaluating N-gram based evaluation metrics for automatic keyphrase extraction. Proceedings of the 23rd International Conference on Computational Linguistics (Coling'10), 2010, Aug 23 - 27, Beijing, China. 2010: 572 - 580.

[40] KOEHN P, HOANG H, BIRCH A, et al. Moses: Open source toolkit for statistical machine translation. Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions, 2007, Jun 25 - 27, Czech, Prague. Stroudsburg, PA, USA: Association for Computational Linguistics, 2007: 177 - 180.

[41] BANERJEE S, LAVIE A. METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. Proceedings of the 2005 ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and / or Summarization (MTSE'05), 2005, Jun 29, Ann Arbor, MI, USA. Stroudsburg, PA, USA: Association for Computational Linguistics, 2005: 65 - 72.

[42] WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 2004, 13(4): 600 - 612.

[43] DONG C, LOY C C, HE K, et al. Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2): 295 - 307.

[44] UNTERTHINER T, VAN STEENKISTE S, KURACH K, et al. FVD: A new metric for video generation. Proceeding of the ICLR 2019 Workshop on Deep Generative Models for Highly Structured Data (DGS'19), 2019, May 6 - 9, New Orleans, LA, USA. 2019: 1 - 9.

[45] HORE A, ZIOU D. Image quality metrics: PSNR vs SSIM. Proceeding of the 20th International Conference on Pattern Recognition, 2010, Aug 23 - 26, Istanbul, Turkey. Piscataway, NJ, USA: IEEE, 2010: 2366 - 2369.

[46] VINCENT E, GRIBONVAL R, FEVOTTE C. Performance measurement in blind audio source separation. IEEE Transactions on Audio Speech and Language Processing, 2006, 14(4): 1462 - 1469.

[47] RIX A W, BEERENDS J G, HOLLIER M P, et al. Perceptual evaluation of speech quality (PESQ)-A new method for speech quality assessment of telephone networks and codecs. Proceeding of the 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2001, May 7 - 11, Salt Lake City, UT, USA. Piscataway, NJ, USA: IEEE, 2001: 749 - 752.

[48] TAAL C H, HENDRIKS R C, HEUSDENS R, et al. A short-time objective intelligibility measure for time-frequency weighted noisy speech. Proceeding of the 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010, Mar 14 - 19, Dallas, TX, USA. Piscataway, NJ, USA: IEEE, 2010: 4214 - 4217.

[49] DONG C, LIANG H T, XU X D, et al. Innovative semantic communication system. ArXiv: 2202. 09595, 2022.

[50] CARNAP R, BAR-HILLEL Y. An outline of a theory of semantic information. TR 247. Cambridge, MA, USA: Research Laboratory of Electronics, Massachusetts Institute of Technology, 1952.

[51] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR'16), 2016, Jun 27 - 30, Las Vegas, NV, USA. Piscataway, NJ, USA: IEEE, 2016: 770 - 778.

[52] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks. Proceeding of the 28th International Conference on Neural Information Processing Systems (NIPS'15): Vol 1, 2015, Dec 7 - 12, Montreal, Canada. Cambridge, MA, USA: MIT Press, 2015: 91 - 99.

[53] MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositionality. Proceeding of the 26th International Conference on Neural Information Processing Systems (NIPS'13): Vol 2, 2013, Dec 5 - 8, Carson City, NV, USA. Red Hook, NY, USA: Curran Associates Inc, 2013: 3111 - 3119.

[54] DEVLIN J, CHANG M W, LEE K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologie: Vol 1, 2019, Jun 2 - 7, Minneapolis, MI, USA. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019: 4171 - 4186.

Intellicise communication system: model-driven semantic communications

Intellicise communication system: model-driven semantic communications

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