A highly reliable encoding and decoding communication framework based on semantic information

Abstract

Increasing research has focused on semantic communication, the goal of which is to convey accurately the meaning instead of transmitting symbols from the sender to the receiver. In this paper, we design a novel encoding and decoding semantic communication framework, which adopts the semantic information and the contextual correlations between items to optimize the performance of a communication system over various channels. On the sender side, the average semantic loss caused by the wrong detection is defined, and a semantic source encoding strategy is developed to minimize the average semantic loss. To further improve communication reliability, a decoding strategy that utilizes the semantic and the context information to recover messages is proposed in the receiver. Extensive simulation results validate the superior performance of our strategies over state-of-the-art semantic coding and decoding policies on different communication channels.

Keywords

Semantic information / Semantic encoding method / Context-based decoding method

References

[1] C.E. Shannon, A mathematical theory of communication, Bell Syst. Tech. J. 27 (3) (1948) 379-423.

[2] D. Wheeler, B. Natarajan,Engineering semantic communication: a survey, arXiv: 2208.06314, 2022.

[3] W. Tong, G.Y. Li, Nine challenges in artificial intelligence and wireless communica-tions for 6G, IEEE Wirel. Commun. 29 (4) (2022) 140-145.

[4] Y. Zhang, P. Zhang, J. Wei, et al., Semantic communication for intelligent devices: architectures and a paradigm, Sci. Sin. Inform. 52 (5) (2022) 907-921.

[5] Y. Zhang, H. Zhao, J. Wei, et al., Context-based semantic communication via dy-namic programming, IEEE Trans. Cogn. Commun. Netw. 8(3) (2022) 1453-1467.

[6] W. Weaver, Recent contributions to the mathematical theory of communication, Math. Theory Commun. 27 (1949) 1-12.

[7] R. Carnap, Y. Bar-Hillel, An outline of a theory of semantic information, Br. J. Philos. Sci. 4 (14) (1953) 230-232.

[8] L. Floridi, Outline of a theory of strongly semantic information, Minds Mach. 14 (2004) 197-221.

[9] S. D’Alfonso, On quantifying semantic information, Information 2(1) (2011) 61-101.

[10] J. Bao, P. Basu, M. Dean, et al., Towards a theory of semantic communication, in: 2011 IEEE Network Science Workshop, IEEE, 2011, pp. 110-117.

[11] P. Basu, J. Bao, M. Dean, et al., Preserving quality of information by using semantic relationships, in: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, IEEE, 2012, pp. 58-63.

[12] M. Kalfa, M. Gok, A. Atalik, B. Tegin, T.M. Duman, O. Arikan, Towards goal-oriented semantic signal processing: applications and future challenges, Digit. Signal Process. 119 (2021) 103-134.

[13] E.C. Strinati, S. Barbarossa, 6G networks: beyond Shannon towards semantic and goal-oriented communications, Comput. Netw. 190 (2021) 107930.

[14] G. Shi, D. Gao, X. Song, J. Chai, M. Yang, X. Xie, L. Li, X. Li, A new communica-tion paradigm: from bit accuracy to semantic fidelity, https://arxiv.org/abs/2101.12649, 2021. (Accessed 13 December 2022).

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

[16] N. Farsad, M. Rao, A. Goldsmith, Deep learning for joint source-channel coding of text, in: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, 2018, pp. 2326-2330.

[17] M. Rao, N. Farsad, A. Goldsmith, Variable length joint source-channel coding of text using deep neural networks, in: 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC, 2018, pp. 1-5.

[18] B. Güler, A. Yener, A. Swami, The semantic communication game, IEEE Transactions on Cognitive Communications and Networking 4( 4) (2018) 787-802.

[19] H. Xie, Z. Qin, G.Y. Li, B.-H. Juang, Deep learning enabled semantic communication systems, IEEE Trans. Signal Process. 69 (2021) 2663-2675.

[20] H. Xie, Z. Qin, A lite distributed semantic communication system for Internet of things, IEEE J. Sel. Areas Commun. 39 (1) (2021) 142-153.

[21] Z. Weng, Z. Qin, G.Y. Li,Semantic communications for speech signals, in: ICC 2021 -IEEE International Conference on Communications, 2021, pp. 1-6.

[22] H. Xie, Z. Qin, G.Y. Li, Task-oriented multi-user semantic communications for VQA, IEEE Wirel. Commun. Lett. 11 (3) (2022) 553-557.

[23] Y. Hua, J. Du, Deep semantic correlation with adversarial learning for cross-modal retrieval, in: 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication, ICEIEC, 2019, pp. 256-259.

[24] D. Huang, X. Tao, F. Gao, J. Lu, Deep learning-based image semantic coding for se-mantic communications, in: 2021 IEEE Global Communications Conference, GLOBE-COM, 2021, pp. 1-6.

[25] G. He, Exploring task-oriented communication in multi-agent system: a deep rein-forcement learning approach, arXiv :2208.10165, 2022.

[26] Y. Liu, S. Jiang, Y. Zhang, K. Cao, L. Zhou, B. Seet, H. Zhao, J. Wei, Extended context-based semantic communication system for text transmission, Digit. Com-mun. Netw. (2022), https://doi.org/10.1016/j.dcan.2022.09.023. (Accessed 13 De-cember 2022).

[27] K. Lu, Q. Zhou, R. Li, Z. Zhao, X. Chen, J. Wu, H. Zhang, Rethinking modern commu-nication from semantic coding to semantic communication, IEEE Wirel. Commun. 30 (1) (2023) 158-164.

[28] T.-Y. Tung, J. Roig Pujol, S. Kobus, D. Gündüz, Effective communications: a joint learning and communication framework for multi-agent reinforcement learning over noisy channels, https://arxiv.org/abs/2101.10369, 2021. (Accessed 13 December 2022).

[29] Y. Liu, Y. Zhang, P. Luo, S. Jiang, K. Cao, J. Wei, Enhancing communication relia-bility from the semantic level under low SNR, Electronics 11 (9) (2022) 1358.

[30] J. Shen, H. Ji, J. Han, Near-imperceptible neural linguistic steganography via self-adjusting arithmetic coding, in: Proc. of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP, 2020, pp. 303—313.

[31] J. Devlin, M.W. Chang, K. Lee, K. Toutanova, BERT: pretraining of deep bidirec-tional transformers for language understanding,in: Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Lin-guistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, 2019, pp. 4171-4186.

[32] Z. Wang, Q. Tang, L. Wang, J. Wei, Joint optimization algorithm for partition scheduling of dynamic partial reconfigurable systems based on simulated anneal-ing, Comput. Sci. 47 (8) (2020) 26-31.

[33] A. Memushaj, T.M. Shbh, Using grapheme n-grams in spelling correction and aug-mentative typing systems, New Math. Nat. Comput. 4(1) (2017) 87-106.

[34] Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. Salakhutdinov, Q.V. Le, XLNet: generalized autoregressive pretraining for language understanding, arXiv preprint, arXiv :1906. 08237, 2019.

[35] K. Papineni, S. Roukos, T. Ward, W. Zhu, BLEU: a method for automatic evaluation of machine translation,in: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, 2002, pp. 311-318.

[36] S. Banerjee, A. Lavie, METEOR: an automatic metric for MT evaluation with im-proved correlation with human judgments,in: Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Sum-marization, 2005, pp. 65-72.

[37] T. Zhang, V. Kishore, F. Wu, K.Q. Weinberger, Y. Artzi, Bertscore: evaluating text generation with BERT, http://arxiv.org/abs/1904.09675, 2020. (Accessed 24 Febru-ary 2020).

[38] C. Lv, H. Liu, Y. Dong, An efficient corpus based part-of-speech tagging with GEP, in: 2010 Sixth International Conference on Semantics, Knowledge and Grids, 2010, pp. 289-292.

[39] N. Bonello, Sheng Chen, L. Hanzo, Low-density parity-check codes and their rateless relatives, IEEE Commun. Surv. Tutor. 13 (1) (2011) 3-26.