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Beamforming Design for Semantic-Bit Coexisting Communication System (2403.11693v3)

Published 18 Mar 2024 in cs.IT, eess.SP, and math.IT

Abstract: Semantic communication (SemCom) is emerging as a key technology for future sixth-generation (6G) systems. Unlike traditional bit-level communication (BitCom), SemCom directly optimizes performance at the semantic level, leading to superior communication efficiency. Nevertheless, the task-oriented nature of SemCom renders it challenging to completely replace BitCom. Consequently, it is desired to consider a semantic-bit coexisting communication system, where a base station (BS) serves SemCom users (sem-users) and BitCom users (bit-users) simultaneously. Such a system faces severe and heterogeneous inter-user interference. In this context, this paper provides a new semantic-bit coexisting communication framework and proposes a spatial beamforming scheme to accommodate both types of users. Specifically, we consider maximizing the semantic rate for semantic users while ensuring the quality-of-service (QoS) requirements for bit-users. Due to the intractability of obtaining the exact closed-form expression of the semantic rate, a data driven method is first applied to attain an approximated expression via data fitting. With the resulting complex transcendental function, majorization minimization (MM) is adopted to convert the original formulated problem into a multiple-ratio problem, which allows fractional programming (FP) to be used to further transform the problem into an inhomogeneous quadratically constrained quadratic programs (QCQP) problem. Solving the problem leads to a semi-closed form solution with undetermined Lagrangian factors that can be updated by a fixed point algorithm. Extensive simulation results demonstrate that the proposed beamforming scheme significantly outperforms conventional beamforming algorithms such as zero-forcing (ZF), maximum ratio transmission (MRT), and weighted minimum mean-square error (WMMSE).

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References (38)
  1. C. E. Shannon and W. Weaver, The mathematical theory of communication. University of illinois Press, 1949.
  2. W. Jiang, B. Han, M. A. Habibi, and H. D. Schotten, “The road towards 6g: A comprehensive survey,” IEEE Open J. Commun. Soc., vol. 2, pp. 334–366, 2021.
  3. W. Saad, M. Bennis, and M. Chen, “A vision of 6g wireless systems: Applications, trends, technologies, and open research problems,” IEEE Netw., vol. 34, no. 3, pp. 134–142, 2019.
  4. H. Xie, Z. Qin, G. Y. Li, and B.-H. Juang, “Deep learning enabled semantic communication systems,” IEEE Trans. Singal Process., vol. 69, pp. 2663–2675, 2021.
  5. E. Bourtsoulatze, D. B. Kurka, and D. Gündüz, “Deep joint source-channel coding for wireless image transmission,” IEEE Trans. Cogn. Commun. Netw., vol. 5, no. 3, pp. 567–579, 2019.
  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 J. Sel. Areas Commun., vol. 40, no. 8, pp. 2300–2316, 2022.
  7. H. Gao, G. Yu, and Y. Cai, “Adaptive modulation and retransmission scheme for semantic communication systems,” IEEE Trans. Cogn. Commun. Netw., 2023.
  8. P. Jiang, C.-K. Wen, S. Jin, and G. Y. Li, “Deep source-channel coding for sentence semantic transmission with HARQ,” IEEE transactions on communications, vol. 70, no. 8, pp. 5225–5240, 2022.
  9. Z. Weng and Z. Qin, “Semantic communication systems for speech transmission,” IEEE J. Sel. Areas Commun., vol. 39, no. 8, pp. 2434–2444, 2021.
  10. S. Wang, J. Dai, Z. Liang, K. Niu, Z. Si, C. Dong, X. Qin, and P. Zhang, “Wireless deep video semantic transmission,” IEEE J. Sel. Areas Commun., vol. 41, no. 1, pp. 214–229, 2022.
  11. G. Zhang, Q. Hu, Z. Qin, Y. Cai, G. Yu, X. Tao, and G. Y. Li, “A unified multi-task semantic communication system for multimodal data,” [Online]. Available: https://arxiv.org/abs/2209.07689, 2022.
  12. Q. Fu, H. Xie, Z. Qin, G. Slabaugh, and X. Tao, “Vector quantized semantic communication system,” IEEE Wireless Commun. Lett., 2023.
  13. T.-Y. Tung, D. B. Kurka, M. Jankowski, and D. Gündüz, “Deepjscc-q: Constellation constrained deep joint source-channel coding,” IEEE J. Sel. Areas Inf. Theory, 2022.
  14. Y. Bo, Y. Duan, S. Shao, and M. Tao, “Learning based joint coding-modulation for digital semantic communication systems,” [Online]. Available: https://arxiv.org/abs/2208.05704, 2022.
  15. 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 Trans. Circuits Syst. Video Technol., vol. 32, no. 4, pp. 2315–2328, 2021.
  16. H. Wu, Y. Shao, K. Mikolajczyk, and D. Gündüz, “Channel-adaptive wireless image transmission with OFDM,” IEEE Wireless Commun. Lett., vol. 11, no. 11, pp. 2400–2404, 2022.
  17. L. Yan, Z. Qin, R. Zhang, Y. Li, and G. Y. Li, “Resource allocation for text semantic communications,” IEEE Wireless Commun. Lett., vol. 11, no. 7, pp. 1394–1398, 2022.
  18. C. Liu, C. Guo, Y. Yang, and N. Jiang, “Adaptable semantic compression and resource allocation for task-oriented communications,” [Online]. Available: https://arxiv.org/abs/2204.08910, 2022.
  19. Q. Hu, G. Zhang, Z. Qin, Y. Cai, G. Yu, and G. Y. Li, “Robust semantic communications with masked VQ-VAE enabled codebook,” IEEE Trans. Wireless Commun., 2023.
  20. M. Zhu, C. Feng, C. Guo, Z. Liu, N. Jiang, and O. Simeone, “Semantics-aware remote estimation via information bottleneck-inspired type based multiple access,” [Online]. Available: https://arxiv.org/abs/2212.09337, 2022.
  21. T. Wu, Z. Chen, M. Tao, B. Xia, and W. Zhang, “Fusion-based multi-user semantic communications for wireless image transmission over degraded broadcast channels,” [Online]. Available: https://arxiv.org/abs/2305.09165, 2023.
  22. X. Mu, Y. Liu, L. Guo, and N. Al-Dhahir, “Heterogeneous semantic and bit communications: A semi-NOMA scheme,” IEEE J. Sel. Areas Commun., vol. 41, no. 1, pp. 155–169, 2022.
  23. W. Li, H. Liang, C. Dong, X. Xu, P. Zhang, and K. Liu, “Non-orthogonal multiple access enhanced multi-user semantic communication,” [Online]. Available: https://arxiv.org/abs/2303.06597, 2023.
  24. Q. Shi, M. Razaviyayn, Z.-Q. Luo, and C. He, “An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel,” IEEE Trans. Singal Process., vol. 59, no. 9, pp. 4331–4340, 2011.
  25. E. Björnson, M. Bengtsson, and B. Ottersten, “Optimal multiuser transmit beamforming: A difficult problem with a simple solution structure [lecture notes],” IEEE Signal Process. Mag., vol. 31, no. 4, pp. 142–148, 2014.
  26. W. Xia, G. Zheng, Y. Zhu, J. Zhang, J. Wang, and A. P. Petropulu, “A deep learning framework for optimization of MISO downlink beamforming,” IEEE Trans. Commun., vol. 68, no. 3, pp. 1866–1880, 2019.
  27. J. Kim, H. Lee, S.-E. Hong, and S.-H. Park, “Deep learning methods for universal MISO beamforming,” IEEE Wireless Commun. Lett., vol. 9, no. 11, pp. 1894–1898, 2020.
  28. Q. Hu, Y. Cai, Q. Shi, K. Xu, G. Yu, and Z. Ding, “Iterative algorithm induced deep-unfolding neural networks: Precoding design for multiuser MIMO systems,” IEEE Trans. Wireless Commun., 2020.
  29. L. Pellaco, M. Bengtsson, and J. Jaldén, “Matrix-inverse-free deep unfolding of the weighted MMSE beamforming algorithm,” IEEE Open J. Commun. Soc., vol. 3, pp. 65–81, 2021.
  30. X. Hu, C. Liu, M. Peng, and C. Zhong, “Irs-based integrated location sensing and communication for mmwave simo systems,” IEEE Trans. Wireless Commun., 2022.
  31. L. Pellaco, M. Bengtsson, and J. Jaldén, “Deep unfolding of the weighted MMSE beamforming algorithm,” [Online]. Available: https://arxiv.org/abs/2006.08448, 2020.
  32. X. Mu and Y. Liu, “Exploiting semantic communication for non-orthogonal multiple access,” IEEE J. Sel. Areas Commun., 2023.
  33. Z. Cheng, H. Sun, M. Takeuchi, and J. Katto, “Deep residual learning for image compression.,” in Proc. of the IEEE conference on computer vision and pattern recognition (CVPR), Long Beach, Canada, 2019.
  34. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 248–255, Miami, Florida, USA, 2009.
  35. M. Zhang, Y. Li, Z. Zhang, G. Zhu, and C. Zhong, “Wireless image transmission with semantic and security awareness,” accepted to appear in IEEE Wireless Commun. Lett., 2023.
  36. K. Shen and W. Yu, “Fractional programming for communication systems—Part I: Power control and beamforming,” IEEE Trans. Singal Process., vol. 66, no. 10, pp. 2616–2630, 2018.
  37. X. Zhao, S. Lu, Q. Shi, and Z.-Q. Luo, “Rethinking WMMSE: Can its complexity scale linearly with the number of BS antennas?,” IEEE Trans. Singal Process., vol. 71, pp. 433–446, 2023.
  38. J. Gao, C. Zhong, G. Y. Li, J. B. Soriaga, and A. Behboodi, “Deep learning-based channel estimation for wideband hybrid mmWave massive MIMO,” IEEE Trans. Commun., 2023.
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