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Semantic Importance-Aware Based for Multi-User Communication Over MIMO Fading Channels (2312.16057v1)

Published 26 Dec 2023 in cs.IT, eess.SP, and math.IT

Abstract: Semantic communication, as a novel communication paradigm, has attracted the interest of many scholars, with multi-user, multi-input multi-output (MIMO) scenarios being one of the critical contexts. This paper presents a semantic importance-aware based communication system (SIA-SC) over MIMO Rayleigh fading channels. Combining the semantic symbols' inequality and the equivalent subchannels of MIMO channels based on Singular Value Decomposition (SVD) maximizes the end-to-end semantic performance through the new layer mapping method. For multi-user scenarios, a method of semantic interference cancellation is proposed. Furthermore, a new metric, namely semantic information distortion (SID), is established to unify the expressions of semantic performance, which is affected by channel bandwidth ratio (CBR) and signal-to-noise ratio (SNR). With the help of the proposed metric, we derived performance expressions and Semantic Outage Probability (SOP) of SIA-SC for Single-User Single-Input Single-Output (SU-SISO), Single-User MIMO (SU-MIMO), Multi-Users SISO (MU-MIMO) and Multi-Users MIMO (MU-MIMO) scenarios. Numerical experiments show that SIA-SC can significantly improve semantic performance across various scenarios.

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References (30)
  1. P. Zhang, X. Xu, C. Dong, S. Han, and W. Bizhu, “Intellicise Communication System: Model-driven Semantic Communications,” The Journal of China Universities of Posts and Telecommunications, vol. 29, no. 1, pp. 2–12, 2022.
  2. P. Zhang, W. Xu, H. Gao, K. Niu, X. Xu, X. Qin, C. Yuan, Z. Qin, H. Zhao, J. Wei, and F. Zhang, “Toward Wisdom-Evolutionary and Primitive-Concise 6G: A New Paradigm of Semantic Communication Networks,” Engineering, vol. 8, pp. 60–73, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2095809921004513
  3. E. Bourtsoulatze, D. Burth Kurka, and D. Gündüz, “Deep Joint Source-Channel Coding for Wireless Image Transmission,” IEEE Transactions on Cognitive Communications and Networking, vol. 5, no. 3, pp. 567–579, 2019.
  4. 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.
  5. C. Dong, H. Liang, X. Xu, S. Han, B. Wang, and P. Zhang, “Semantic Communication System Based on Semantic Slice Models Propagation,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 202–213, 2023.
  6. J. Dai, S. Wang, K. Tan, Z. Si, X. Qin, K. Niu, and P. Zhang, “Nonlinear Transform Source-Channel Coding for Semantic Communications,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 8, pp. 2300–2316, 2022.
  7. Z. Bao, H. Liang, C. Dong, X. Xu, and G. Liu, “MDVSC–Wireless Model Division Video Semantic Communication,” in 2023 IEEE Globecom Workshops (GC Wkshps), 2023.
  8. J. Ballé, D. Minnen, S. Singh, S. J. Hwang, and N. Johnston, “Variational Image Compression with a Scale Hyperprior,” arXiv preprint arXiv:1802.01436, 2018.
  9. D. Huang, F. Gao, X. Tao, Q. Du, and J. Lu, “Toward Semantic Communications: Deep Learning-Based Image Semantic Coding,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 55–71, 2023.
  10. B. Zhang, Z. Qin, and G. Y. Li, “Semantic Communications with Variable-Length Coding for Extended Reality,” IEEE Journal of Selected Topics in Signal Processing, pp. 1–14, 2023.
  11. E. Telatar, “Capacity of Multi-antenna Gaussian Channels,” European transactions on telecommunications, vol. 10, no. 6, pp. 585–595, 1999.
  12. G. J. Foschini and M. J. Gans, “On Limits of Wireless Communications in a Fading Environment when using Multiple Antennas,” Wireless personal communications, vol. 6, pp. 311–335, 1998.
  13. Y. Shi, S. Shao, Y. Wu, W. Zhang, X.-G. Xia, and C. Xiao, “Excess Distortion Exponent Analysis for Semantic-Aware MIMO Communication Systems,” IEEE Transactions on Wireless Communications, pp. 1–1, 2023.
  14. H. Wu, Y. Shao, C. Bian, K. Mikolajczyk, and D. Gündüz, “Vision Transformer for Adaptive Image Transmission over MIMO Channels,” arXiv preprint arXiv:2210.15347, 2022.
  15. S. Yao, S. Wang, J. Dai, K. Niu, and P. Zhang, “Versatile Semantic Coded Transmission over MIMO Fading Channels,” arXiv preprint arXiv:2210.16741, 2022.
  16. H. Liang, K. Liu, X. Liu, H. Jiang, C. Dong, X. Xu, K. Liu, P. Zhang, and K. Liu, “Orthogonal Model Division Multiple Access,” submitted to IEEE Transactions on Wireless Communications.
  17. P. Zhang, X. Xu, C. Dong, K. Niu, H. Liang, Z. Liang, X. Qin, M. Sun, H. Chen, N. Ma, W. Xu, G. Wang, and X. Tao, “Model Division Multiple Access for Semantic Communications,” Frontiers of Information Technology & Electronic Engineering, vol. 24, no. 801–812, 2023.
  18. X. Mu, Y. Liu, L. Guo, and N. Al-Dhahir, “Heterogeneous Semantic and Bit Communications: A Semi-NOMA Scheme,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 155–169, 2023.
  19. L. Yan, Z. Qin, R. Zhang, Y. Li, and G. Y. Li, “Resource Allocation for Text Semantic Communications,” IEEE Wireless Communications Letters, vol. 11, no. 7, pp. 1394–1398, 2022.
  20. W. Li, H. Liang, C. Dong, X. Xu, P. Zhang, and K. Liu, “Non-Orthogonal Multiple Access Enhanced Multi-User Semantic Communication,” IEEE Transactions on Cognitive Communications and Networking, pp. 1–1, 2023.
  21. S. Wang, J. Dai, Z. Liang, K. Niu, Z. Si, C. Dong, X. Qin, and P. Zhang, “Wireless Deep Video Semantic Transmission,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 214–229, 2023.
  22. D. B. Kurka and D. Gündüz, “DeepJSCC-f: Deep Joint Source-Channel Coding of Images With Feedback,” IEEE Journal on Selected Areas in Information Theory, vol. 1, no. 1, pp. 178–193, 2020.
  23. J. Ballé, D. Minnen, S. Singh, S. J. Hwang, and N. Johnston, “Variational Image Compression with a Scale Hyperprior,” in International Conference on Learning Representations, 2018. [Online]. Available: https://openreview.net/forum?id=rkcQFMZRb
  24. Z. Ding, Y. Liu, J. Choi, Q. Sun, M. Elkashlan, I. Chih-Lin, and H. V. Poor, “Application of non-orthogonal multiple access in lte and 5g networks,” IEEE Communications Magazine, vol. 55, no. 2, pp. 185–191, 2017.
  25. Z. Ding, M. Peng, and H. V. Poor, “Cooperative non-orthogonal multiple access in 5g systems,” IEEE Communications Letters, vol. 19, no. 8, pp. 1462–1465, 2015.
  26. G. Zhang, Q. Hu, Y. Cai, and G. Yu, “Scan: Semantic communication with adaptive channel feedback,” arXiv preprint arXiv:2306.15534, 2023.
  27. R. Benenson, S. Popov, and V. Ferrari, “Large-scale interactive object segmentation with human annotators,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.
  28. “Kodak photocd dataset,” https://r0k.us/graphics/kodak/, 1993.
  29. Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600–612, 2004.
  30. S. R. Islam, N. Avazov, O. A. Dobre, and K. Kwak, “Power-domain non-orthogonal multiple access (NOMA) in 5G systems: Potentials and challenges,” IEEE Communications Surveys & Tutorials, vol. 19, no. 2, pp. 721–742, 2016.

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