Harnessing the Power of AI-Generated Content for Semantic Communication
Abstract: Semantic Communication (SemCom) is envisaged as the next-generation paradigm to address challenges stemming from the conflicts between the increasing volume of transmission data and the scarcity of spectrum resources. However, existing SemCom systems face drawbacks, such as low explainability, modality rigidity, and inadequate reconstruction functionality. Recognizing the transformative capabilities of AI-generated content (AIGC) technologies in content generation, this paper explores a pioneering approach by integrating them into SemCom to address the aforementioned challenges. We employ a three-layer model to illustrate the proposed AIGC-assisted SemCom (AIGC-SCM) architecture, emphasizing its clear deviation from existing SemCom. Grounded in this model, we investigate various AIGC technologies with the potential to augment SemCom's performance. In alignment with SemCom's goal of conveying semantic meanings, we also introduce the new evaluation methods for our AIGC-SCM system. Subsequently, we explore communication scenarios where our proposed AIGC-SCM can realize its potential. For practical implementation, we construct a detailed integration workflow and conduct a case study in a virtual reality image transmission scenario. The results demonstrate our ability to maintain a high degree of alignment between the reconstructed content and the original source information, while substantially minimizing the data volume required for transmission. These findings pave the way for further enhancements in communication efficiency and the improvement of Quality of Service. At last, we present future directions for AIGC-SCM studies.
- W. Yang, H. Du, Z. Q. Liew, W. Y. B. Lim, Z. Xiong, D. Niyato, X. Chi, X. Shen, and C. Miao, “Semantic communications for future internet: Fundamentals, applications, and challenges,” IEEE Commun. Surveys and Tutorials, vol. 25, no. 1, pp. 213–250, 1st Quart., 2023.
- Y. Cao, S. Li, Y. Liu, Z. Yan, Y. Dai, P. S. Yu, and L. Sun, “A comprehensive survey of AI-generated content (AIGC): A history of generative AI from GAN to ChatGPT,” arXiv preprint arXiv:2303.04226, 2023.
- Y. Li, H. Liu, Q. Wu, F. Mu, J. Yang, J. Gao, C. Li, and Y. J. Lee, “GLIGEN: Open-set grounded text-to-image generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, Canada, Jun. 2023, pp. 22 511–22 521.
- L. Xia, Y. Sun, C. Liang, L. Zhang, M. A. Imran, and D. Niyato, “Generative AI for semantic communication: Architecture, challenges, and outlook,” arXiv preprint arXiv:2308.15483, 2024.
- Y. Xie, M. Yuan, B. Dong, and Q. Li, “Diffusion model for generative image denoising,” arXiv preprint arXiv:2302.02398, 2023.
- W. Yang, Z. Xiong, Y. Yuan, and T. Q. Quek, “Semantic change driven generative semantic communication framework,” arXiv preprint arXiv:2309.12775, 2023.
- J. Wang, H. Du, D. Niyato, J. Kang, Z. Xiong, D. Rajan, S. Mao, Xuemin, and Shen, “A unified framework for guiding generative AI with wireless perception in resource constrained mobile edge networks,” arXiv preprint arXiv:2309.01426, 2023.
- C.-A. Brust and J. Denzler, “Not just a matter of semantics: the relationship between visual similarity and semantic similarity,” in German Conference on Pattern Recognition, Dortmund, Germany, Sep. 2019.
- A. Radford et al., “Learning transferable visual models from natural language supervision,” in International Conference on Machine Learning, Jeju Island, Republic of Korea, Jul. 2021, pp. 8748–8763.
- R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, Louisiana, Jun. 2022, pp. 10 684–10 695.
- G. Jocher, A. Chaurasia, and J. Qiu, “YOLO by Ultralytics,” Jan. 2023. [Online]. Available: https://github.com/ultralytics/ultralytics
- N. Ruiz, Y. Li, V. Jampani, Y. Pritch, M. Rubinstein, and K. Aberman, “Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, Canada, Jun. 2023, pp. 22 500–22 510.
- C. Schuhmann, R. Vencu, R. Beaumont, R. Kaczmarczyk, C. Mullis, A. Katta, T. Coombes, J. Jitsev, and A. Komatsuzaki, “LAION-400M: Open dataset of clip-filtered 400 million image-text pairs,” arXiv preprint arXiv:2111.02114, 2021.
- T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft COCO: Common objects in context,” in Computer Vision–ECCV, Zurich, Switzerland, Sep. 2014, pp. 740–755.
- K. Yang, S. Wang, J. Dai, K. Tan, K. Niu, and P. Zhang, “WITT: A wireless image transmission transformer for semantic communications,” in IEEE International Conference on Acoustics, Speech and Signal Processing, Rhodes Island, Greece, Jun. 2023, pp. 1–5.
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