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Knowledge-enhanced semantic communication system with OFDM transmissions

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Abstract

As a promising technology to enable effective multi-modal transmission over wireless channels, semantic communication has attracted a lot of attention from academics and industries. Different from Shannon’s information theory, based on common background knowledge provided by the knowledge base, the goal of semantic communication is transmitting intended useful information from the transmitter and recovered by the receiver at the semantic level. However, the existing studies on semantic communication rarely emphasize the essence and the usage of the knowledge base. In this paper, we propose a knowledge-enhanced semantic communication (KESC) system, where the knowledge base is cloud-edge-device collaborative cached. To solve the problem that float-type symbols are difficult to transmit directly through a radio frequency (RF) system, we adopt orthogonal frequency division multiplexing (OFDM) to transmit semantic vectors directly without some traditional signal processing techniques in semantic information transmission, and the semantic pilot is designed to assist semantic reception. Furthermore, we formulate a multi-encoder transformer based neural network model for the KESC system to support text transmission (KESC-T), where the decoder is implemented with a knowledge graph to enhance the performance of semantic decoding. Besides, we define knowledge-enhanced efficiency (KEE) to measure the gain in semantic recovery accuracy brought by per unit of knowledge. Simulation results demonstrate that the recovery accuracy of our proposed KESC outperforms the compared scheme, especially in low signal-to-noise ratio (SNR) or resource-constrained scenarios.

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References

  1. You X, Wang C X, Huang J, et al. Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts. Sci China Inf Sci, 2021, 64: 110301

    Article  Google Scholar 

  2. Calvanese Strinati E, Barbarossa S. 6G networks: beyond Shannon towards semantic and goal-oriented communications. Comput Netw, 2021, 190: 107930

    Article  Google Scholar 

  3. Popovski P, Simeone O, Boccardi F, et al. Semantic-effectiveness filtering and control for post-5G wireless connectivity. J Ind Inst Sci, 2020, 100: 435–443

    Article  Google Scholar 

  4. Juba B, Sudan M. Universal semantic communication II: a theory of goal-oriented communication. In: Proceedings of Electronic Colloquium on Computational Complexity (ECCC), 2008

  5. Goldreich O, Juba B, Sudan M. A theory of goal-oriented communication. J ACM, 2012, 59: 1–65

    Article  MathSciNet  MATH  Google Scholar 

  6. Zhang Y C, Zhang P, Wei J B, et al. Semantic communication for intelligent devices: architectures and a paradigm (in Chinese). Sci Sin Inform, 2022, 52: 907–921

    Article  Google Scholar 

  7. Xie H, Qin Z, Li G Y, et al. Deep learning enabled semantic communication systems. IEEE Trans Signal Process, 2021, 69: 2663–2675

    Article  MathSciNet  MATH  Google Scholar 

  8. Papineni K, Roukos S, Ward T, et al. BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, 2002. 311–318

  9. Klakow D, Peters J. Testing the correlation of word error rate and perplexity. Speech Commun, 2002, 38: 19–28

    Article  MATH  Google Scholar 

  10. Xie H, Qin Z. A lite distributed semantic communication system for Internet of Things. IEEE J Sel Areas Commun, 2021, 39: 142–153

    Article  Google Scholar 

  11. Weng Z, Qin Z, and Li G Y. Semantic communications for speech signals. In: Proceedings of ICC 2021-IEEE International Conference on Communications, 2021. 1–6

  12. Xie H, Qin Z, Li G Y. Task-oriented multi-user semantic communications for VQA. IEEE Wirel Commun Lett, 2022, 11: 553–557

    Article  Google Scholar 

  13. Shi G M, Gao D H, Song X D, et al. A new communication paradigm: from bit accuracy to semantic fidelity. 2021. ArXiv:2101.12649

  14. Shi G, Xiao Y, Li Y, et al. From semantic communication to semantic-aware networking: model, architecture, and open problems. IEEE Commun Mag, 2021, 59: 44–50

    Article  Google Scholar 

  15. Zhang P, Xu W, Gao H, et al. Toward wisdom-evolutionary and primitive-concise 6G: a new paradigm of semantic communication networks. Engineering, 2022, 8: 60–73

    Article  Google Scholar 

  16. Ji S, Pan S, Cambria E, et al. A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans Neural Netw Learn Syst, 2022, 33: 494–514

    Article  MathSciNet  Google Scholar 

  17. Zhang Z, Han X, Liu Z, et al. ERNIE: enhanced language representation with informative entities. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019. 1441–1451

  18. Yang M, Bian C, Kim H S. Deep joint source channel coding for wireless image transmission with OFDM. In: Proceedings of ICC 2021-IEEE International Conference on Communications, 2021. 1–6

  19. Weaver W. Recent contributions to the mathematical theory of communication. ETC Rev Gen Semant, 1953, 10: 261–281

    Google Scholar 

  20. Bao J, Basu P, Dean M, et al. Towards a theory of semantic communication. In: Proceedings of IEEE Network Science Workshop, 2011. 110–117

  21. Zhang P, Xu X, Dong C, et al. Intellicise communication system: model-driven semantic communications. J China Univ Post Telecommun, 2022, 29: 2

    Google Scholar 

  22. Zhou F, Li Y, Zhang X, et al. Cognitive semantic communication systems driven by knowledge graph. In: Proceedings of IEEE International Conference on Communications, 2022

  23. Yang W, Liew Z Q, Lim W Y B, et al. Semantic communication meets edge intelligence. IEEE Wirel Commun, 2022, 29: 28–35

    Article  Google Scholar 

  24. Devlin J, Chang M, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, 2019. 4171–4186

  25. Liu W, Zhou P, Zhao Z, et al. K-bert: enabling language representation with knowledge graph. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2020. 2901–2908

  26. Liu Y, Wan Y, He L, et al. KG-BART: knowledge graph-augmented bart for generative commonsense reasoning. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2021. 6418–6425

  27. Lu Y, Zhang J, Zong C. Exploiting knowledge graph in neural machine translation. In: Proceedings of China Workshop on Machine Translation, 2018. 27–38

  28. Moussallem D, Arčan M, Ngomo A C N, et al. Augmenting neural machine translation with knowledge graphs. 2019. ArXiv:1902.08816

  29. Zhao Y, Xiang L, Zhu J, et al. Knowledge graph enhanced neural machine translation via multi-task learning on sub-entity granularity. In: Proceedings of the 28th International Conference on Computational Linguistics, 2020. 4495–4505

  30. Zhao Y, Zhang J, Zhou Y, et al. Knowledge graphs enhanced neural machine translation. In: Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence, 2021. 4039–4045

  31. Xie S, Xia Y, Wu L, et al. End-to-end entity-aware neural machine translation. Mach Learn, 2022, 111: 1181–1203

    Article  MathSciNet  Google Scholar 

  32. Li B, Liu H, Wang Z, et al. Does multi-encoder help? A case study on context-aware neural machine translation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020. 3512–3518

  33. Libovickỳ J, Helcl J, Mareček D. Input combination strategies for multi-source transformer decoder. In: Proceedings of the 3rd Conference on Machine Translation: Research Papers, 2018

  34. Shin J, Lee J H. Multi-encoder transformer network for automatic post-editing. In: Proceedings of the 3rd Conference on Machine Translation: Shared Task Papers, 2018. 840–845

  35. Li Y, Feng R, Rehg I, et al. Transformer-based neural text generation with sntactic guidance. 2020. ArXiv:2010.01737

  36. Lohrenz T, Li Z, Fingscheidt T. Multi-encoder learning and stream fusion for transformer-based end-to-end automatic speech recognition. 2021. ArXiv:2104.00120

  37. Lin T, Wang Y, Liu X, et al. A surve of transformers. AI Open, 2022, 3: 111–132

    Article  Google Scholar 

  38. Vaswani A, Shazeer N, Parmar N, et al. Attention is all ou need. In: Proceedings of the 31st International Conference on Neural Information Processing S stems, 2017

  39. Vashishth S, Jain P, Talukdar P. CESI: canonicalizing open knowledge bases using embeddings and side information. In: Proceedings of World Wide Web Conference, 2018. 1317–1327

  40. Fader A, Soderland S, Etzioni O. Identif ing relations for open information extraction. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, 2011. 1535–1545

  41. Callan J, Ho M, Yoo C, et al. Clueweb09 data set. 2009. https://lemurproject.org/clueweb09/index.php

  42. Pennington J, Socher R, Manning C D. GloVe: global vectors for word representation. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014. 1532–1543

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Acknowledgements

This work was supported in part by Key R&D Program of Shandong Province (Grant No. 2020CXGC010109), National Natural Science Foundation of China (Grant No. 62201079), Fundamental Research Funds for the Central Universities (Grant No. 2022RC15), and Major Key Project of PCL.

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Correspondence to Shujun Han.

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Xu, X., Xiong, H., Wang, Y. et al. Knowledge-enhanced semantic communication system with OFDM transmissions. Sci. China Inf. Sci. 66, 172302 (2023). https://doi.org/10.1007/s11432-022-3624-4

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  • DOI: https://doi.org/10.1007/s11432-022-3624-4

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