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Semantic Communications and Networking

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Fundamentals of 6G Communications and Networking

Part of the book series: Signals and Communication Technology ((SCT))

Abstract

Until fifth-generation communications, researchers focused on transmitting the data fast and accurately for many users. For this, many studies have been shown around the relay system, with achievable rate maximization considering the co-interference. These are the first Shannon communication level, which focuses on bit transmission. Spurred by the advance of deep learning, the fusion of artificial intelligence (AI) and communications or networks emerged. In the second and third level of the Shannon-Weaver model, contextual meaning and reasoning are the objectives of transmission. Fortunately, AI has shown tremendous empirical results for contextual reasoning. In the physical (PHY) layer, joint source-channel coding (JSCC) is studied broadly. The variational autoencoder shows a strong background in research. In this chapter, we introduce the concepts of semantic communication (SC) and semantic networking. In addition, we introduce the performance enhancements such as loss of information, rate maximization, and robustness of noisy channels. Moreover, we provide the performance gain cited from research papers.

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References

  1. Y. C. Eldar, A. Goldsmith, D. Gündüz, and H. V. Poor, Machine Learning and Wireless Communications. Cambridge University Press, 2022.

    Book  Google Scholar 

  2. Q. Lan, D. Wen, Z. Zhang, Q. Zeng, X. Chen, P. Popovski, and K. Huang, “What is semantic communication? a view on conveying meaning in the era of machine intelligence,” J. Commun. Inf. Networks, vol. 6, pp. 336–371, 2021.

    Article  Google Scholar 

  3. C. E. Shannon, “A mathematical theory of communication,” The Bell System Technical Journal, vol. 27, no. 3, pp. 379–423, 1948.

    Article  MathSciNet  MATH  Google Scholar 

  4. J. Park, S. Samarakoon, M. Bennis, and M. Debbah, “Wireless network intelligence at the edge,” Proceedings of the IEEE, vol. 107, no. 11, pp. 2204–2239, October 2019.

    Article  Google Scholar 

  5. H. Xie, Z. Qin, G. Y. Li, and B.-H. Juang, “Deep learning enabled semantic communication systems,” IEEE Transactions on Signal Processing, vol. 69, pp. 2663–2675, 2021.

    Article  MathSciNet  MATH  Google Scholar 

  6. Q. Lan, D. Wen, Z. Zhang, Q. Zeng, X. Chen, P. Popovski, and K. Huang, “What is semantic communication? a view on conveying meaning in the era of machine intelligence,” Journal of Communications and Information Networks, vol. 6, no. 4, pp. 336–371, 2021.

    Article  Google Scholar 

  7. G. Shi, Y. Xiao, Y. Li, and X. Xie, “From semantic communication to semantic-aware networking: Model, architecture, and open problems,” IEEE Communications Magazine, vol. 59, no. 8, pp. 44–50, 2021.

    Article  Google Scholar 

  8. Z. Weng and Z. Qin, “Semantic communication systems for speech transmission,” IEEE Journal on Selected Areas in Communications, vol. 39, pp. 2434–2444, 2021.

    Article  Google Scholar 

  9. Q. Zhou, R. Li, Z. Zhao, C. Peng, and H. Zhang, “Semantic communication with adaptive universal transformer,” IEEE Wireless Communications Letters, vol. 11, no. 3, pp. 453–457, 2022.

    Article  Google Scholar 

  10. J. Foerster, I. A. Assael, N. de Freitas, and S. Whiteson, “Learning to communicate with deep multi-agent reinforcement learning,” in Proc. of the Advances in Neural Information Processing Systems (NeurIPS), vol. 29, NY, USA, December 2016, pp. 2145–2153.

    Google Scholar 

  11. W. J. Yun, B. Lim, S. Jung, Y.-C. Ko, J. Park, J. Kim, and M. Bennis, “Attention-based reinforcement learning for real-time UAV semantic communication,” in Proc. IEEE International Symposium on Wireless Communication Systems (ISWCS). IEEE, 2021, pp. 1–6.

    Google Scholar 

  12. H. Seo, J. Park, M. Bennis, and M. Debbah, “Semantics-native communication with contextual reasoning,” CoRR, vol. abs/2108.05681, 2021.

    Google Scholar 

  13. J. Choi, S. W. Loke, and J. Park, “A unified view on semantic information and communication: A probabilistic logic approach,” CoRR, vol. abs/2201.05936, 2022.

    Google Scholar 

  14. K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural networks, vol. 2, no. 5, pp. 359–366, 1989.

    Article  MATH  Google Scholar 

  15. D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” CoRR, vol. abs/1312.6114, 2013.

    Google Scholar 

  16. A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey, “Adversarial autoencoders,” CoRR, vol. abs/1511.05644, 2015.

    Google Scholar 

  17. Y. M. Saidutta, A. Abdi, and F. Fekri, “Joint source-channel coding over additive noise analog channels using mixture of variational autoencoders,” IEEE Journal on Selected Areas in Communications, vol. 39, pp. 2000–2013, 2021.

    Article  Google Scholar 

  18. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017.

    Google Scholar 

  19. A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” CoRR, vol. abs/2010.11929, 2020.

    Google Scholar 

  20. S. Iqbal and F. Sha, “Actor-attention-critic for multi-agent reinforcement learning,” in International conference on machine learning. PMLR, 2019, pp. 2961–2970.

    Google Scholar 

  21. L. Chen, K. Lu, A. Rajeswaran, K. Lee, A. Grover, M. Laskin, P. Abbeel, A. Srinivas, and I. Mordatch, “Decision transformer: Reinforcement learning via sequence modeling,” CoRR, vol. abs/2106.01345, 2021.

    Google Scholar 

  22. A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, “Hierarchical text-conditional image generation with clip latents,” 2022.

    Google Scholar 

  23. H. Xie, Z. Qin, G. Y. Li, and B.-H. Juang, “Deep learning enabled semantic communication systems,” IEEE Transactions on Signal Processing, vol. 69, pp. 2663–2675, 2021.

    Article  MathSciNet  MATH  Google Scholar 

  24. M. Fresia, F. Perez-Cruz, H. V. Poor, and S. Verdu, “Joint source and channel coding,” IEEE Signal Processing Magazine, vol. 27, no. 6, pp. 104–113, 2010.

    Google Scholar 

  25. J. L. Massey, “Joint source and channel coding,” MASSACHUSETTS INST OF TECH CAMBRIDGE ELECTRONIC SYSTEMS LAB, Tech. Rep., 1977.

    Google Scholar 

  26. T. Richardson and R. Urbanke, Modern coding theory. Cambridge University Press, 2008.

    Book  MATH  Google Scholar 

  27. N. Farsad, M. Rao, and A. J. Goldsmith, “Deep learning for joint source-channel coding of text,” 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2326–2330, 2018.

    Google Scholar 

  28. K. Choi, K. Tatwawadi, A. Grover, T. Weissman, and S. Ermon, “Neural joint source-channel coding,” in Proc. International Conference on Machine Learning (ICML), 2019.

    Google Scholar 

  29. Y. Jiang, H. Kim, H. Asnani, S. Kannan, S. Oh, and P. Viswanath, “Turbo autoencoder: Deep learning based channel codes for point-to-point communication channels,” CoRR, vol. abs/1911.03038, 2019.

    Google Scholar 

  30. P. Jiang, C.-K. Wen, S. Jin, and G. Y. Li, “Deep source-channel coding for sentence semantic transmission with HARQ,” CoRR, vol. abs/2106.03009, 2022.

    Google Scholar 

  31. Q. Zhou, R. Li, Zhifeng Zhao, Yong Xiao, Honggang Zhang, “Adaptive bit rate control in semantic communication with incremental knowledge-based HARQ,” CoRR, vol. abs/2203.06634, 2022.

    Google Scholar 

  32. H. Ye, L. Liang, and G. Y. Li, “Circular convolutional auto-encoder for channel coding,” Proc. International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 1–5, 2019.

    Google Scholar 

  33. J. Xu, B. Ai, W. Chen, A. Yang, P. Sun, and M. Rodrigues, “Wireless image transmission using deep source channel coding with attention modules,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 4, pp. 2315–2328, 2022.

    Article  Google Scholar 

  34. H. Kim, S. Oh, and P. Viswanath, “Physical layer communication via deep learning,” IEEE Journal on Selected Areas in Information Theory, vol. 1, pp. 5–18, 2020.

    Article  Google Scholar 

  35. K. Lu, R. Li, X. Chen, Z. Zhao, and H. Zhang, “Reinforcement learning-powered semantic communication via semantic similarity,” CoRR, vol. abs/2108.12121, 2021.

    Google Scholar 

  36. C. Liu, C. Guo, Y. Yang, and N. Jiang, “Adaptable semantic compression and resource allocation for task-oriented communications,” CoRR, vol. abs/2204.08910, 2022.

    Google Scholar 

  37. Z. Zhang, Q. Yang, S. He, M. Sun, and J. Chen, “Wireless transmission of images with the assistance of multi-level semantic information,” CoRR, vol. abs/2202.04754, 2022.

    Google Scholar 

  38. “Semantic filtering.” [Online]. Available: https://www.ibm.com/docs/en/odm/8.9.2?topic=tree-semantic-filtering

  39. R. Yates, Y. Sun, D. Brown, S. Kaul, E. Modiano, and S. Ulukus, “Age of information: An introduction and survey,” IEEE Journal on Selected Areas in Communications, vol. 39, pp. 1183–1210, 2020.

    Article  Google Scholar 

  40. P. Mankar, Z. Chen, M. A. Abd-Elmagid, N. Pappas, and H. S. Dhillon, “Throughput and age of information in a cellular-based IoT network,” IEEE Transactions on Wireless Communications, vol. 20, pp. 8248–8263, 2020.

    Article  Google Scholar 

  41. O. Ozel, A. Yener, and S. Ulukus, “State amplification and masking while timely updating,” CoRR, vol. abs/2202.11682, 2022.

    Google Scholar 

  42. M. A. Abd-Elmagid and H. S. Dhillon, “Closed-form characterization of the MGF of AoI in energy harvesting status update systems,” IEEE Transactions on Information Theory, vol. 68, no. 6, pp. 3896–3919, 2022.

    Article  MathSciNet  MATH  Google Scholar 

  43. A. Arafa, J. Yang, S. Ulukus, and H. Poor, “Timely status updating over erasure channels using an energy harvesting sensor: Single and multiple sources,” IEEE Transactions on Green Communications and Networking, vol. 6, pp. 6–19, 2021.

    Article  Google Scholar 

  44. M. Hatami, M. Leinonen, Z. Chen, N. Pappas, and M. Codreanu, “On-demand AoI minimization in resource-constrained cache-enabled IoT networks with energy harvesting sensors,” CoRR, vol. abs/2201.12277, 2022.

    Google Scholar 

  45. A. Jaiswal, A. Chattopadhyay, and A. Varma, “Age-of-information minimization via opportunistic sampling by an energy harvesting source,” CoRR, vol. abs/2201.02787, 2022.

    Google Scholar 

  46. M. Hatami, M. Leinonen, and M. Codreanu, “AoI minimization in status update control with energy harvesting sensors,” IEEE Transactions on Communications, vol. 69, pp. 8335–8351, 2020.

    Article  Google Scholar 

  47. N. Pappas and M. Kountouris, “Goal-oriented communication for real-time tracking in autonomous systems,” in 2021 IEEE International Conference on Autonomous Systems (ICAS), 2021, pp. 1–5.

    Google Scholar 

  48. E. Fountoulakis, M. Codreanu, A. Ephremides, and N. Pappas, “Joint sampling and transmission policies for minimizing cost under AoI constraints,” CoRR, vol. abs/2103.15450, 2021.

    Google Scholar 

  49. P. Popovski, O. Simeone, F. Boccardi, D. Gündüz, and O. Sahin, “Semantic-effectiveness filtering and control for post-5G wireless connectivity,” Journal of the Indian Institute of Science, vol. 100, pp. 435–443, 2020.

    Article  Google Scholar 

  50. M. Kountouris and N. Pappas, “Semantics-empowered communication for networked intelligent systems,” IEEE Communications Magazine, vol. 59, no. 6, pp. 96–102, 2021.

    Article  Google Scholar 

  51. P. Agheli, N. Pappas, and M. Kountouris, “Semantics-aware source coding in status update systems,” CoRR, vol. abs/2203.08508, 2022.

    Google Scholar 

  52. G. Stamatakis, N. Pappas, A. Fragkiadakis, and A. Traganitis, “Semantics-aware active fault detection in status updating systems,” CoRR, vol. 2202.00923, 2022.

    Google Scholar 

  53. X. Kang, B. Song, J. Guo, Z. Qin, and F. R. Yu, “Task-oriented image transmission for scene classification in unmanned aerial systems,” IEEE Transactions on Communications, vol. 70, no. 8, pp. 5181–5192, 2022.

    Article  Google Scholar 

  54. H. Xie and Z. Qin, “A lite distributed semantic communication system for internet of things,” IEEE Journal on Selected Areas in Communications, vol. 39, pp. 142–153, 2020.

    Article  Google Scholar 

  55. H. Xie, Z. Qin, X. Tao, and K. Letaief, “Task-oriented multi-user semantic communications,” CoRR, vol. abs/2112.10255, 2021.

    Google Scholar 

  56. K. M. Fisher, “Semantic networking: The new kid on the block,” Journal of research in science teaching, vol. 27, no. 10, pp. 1001–1018, 1990.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Korea University, Seoul, South Korea

    Won Joon Yun, Soohyun Park, Young-Chai Ko & Joongheon Kim

  2. Ewha Womans University, Seoul, South Korea

    Rhoan Lee

  3. Deakin University, Geelong, Australia

    Jihong Park

Authors
  1. Won Joon Yun
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  2. Soohyun Park
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  4. Jihong Park
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  5. Young-Chai Ko
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  6. Joongheon Kim
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Corresponding author

Correspondence to Joongheon Kim .

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Editors and Affiliations

  1. NVIDIA, Santa Clara, CA, USA

    Xingqin Lin

  2. Department of ECE, Hong Kong University of Science and Technology, Kowloon, Hong Kong

    Jun Zhang

  3. Queen Mary University of London, London, UK

    Yuanwei Liu

  4. Korea University, Seoul, Korea (Republic of)

    Joongheon Kim

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Yun, W.J., Park, S., Lee, R., Park, J., Ko, YC., Kim, J. (2024). Semantic Communications and Networking. In: Lin, X., Zhang, J., Liu, Y., Kim, J. (eds) Fundamentals of 6G Communications and Networking. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-37920-8_29

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  • DOI: https://doi.org/10.1007/978-3-031-37920-8_29

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