Fair Resource Allocation for Probabilistic Semantic Communication in IIoT (2407.02922v2)
Abstract: In this paper, the problem of minimum rate maximization for probabilistic semantic communication (PSCom) in industrial Internet of Things (IIoT) is investigated. In the considered model, users employ semantic information extraction techniques to compress the original data before sending it to the base station (BS). During this semantic compression process, knowledge graphs are employed to represent the semantic information, and the probability graph sharing between users and the BS is utilized to further compress the knowledge graph. The semantic compression process can significantly reduce the transmitted data size, but it inevitably introduces additional computation overhead. Considering the limited power budget of the user, we formulate a joint communication and computation optimization problem is formulated aiming to maximize the minimum equivalent rate among all users while meeting total power and semantic compression ratio constraints. To address this problem, two algorithms with different computational complexities are proposed to obtain suboptimal solutions. One algorithm is based on a prorate distribution of transmission power, while the other traverses the combinations of semantic compression ratios among all users. In both algorithms, bisection is employed in order to achieve the greatest minimum equivalent rate. The simulation results validate the effectiveness of the proposed algorithms.
- S. Mumtaz, A. Alsohaily, Z. Pang, A. Rayes, K. F. Tsang, and J. Rodriguez, “Massive internet of things for industrial applications: Addressing wireless iiot connectivity challenges and ecosystem fragmentation,” IEEE Industrial Electronics Magazine, vol. 11, pp. 28–33, Mar. 2017.
- Z. Zhao, Z. Yang, C. Huang, L. Wei, Q. Yang, C. Zhong, W. Xu, and Z. Zhang, “A joint communication and computation design for distributed RISs assisted probabilistic semantic communication in IIoT,” IEEE Internet Things J., Jun. 2024.
- C. Liang, H. Du, Y. Sun, D. Niyato, J. Kang, D. Zhao, and M. A. Imran, “Generative AI-driven semantic communication networks: Architecture, technologies and applications,” arXiv preprint arXiv:2401.00124, Jan. 2024.
- N. Farsad, M. Rao, and A. Goldsmith, “Deep learning for joint source-channel coding of text,” in 2018 IEEE Int. Conf. Acoust. Speech. Signal. Process. (ICASSP), Sept. 2018, pp. 2326–2330.
- S. Yao, K. Niu, S. Wang, and J. Dai, “Semantic coding for text transmission: An iterative design,” IEEE Trans. Cogn. Commun. Netw., vol. 8, no. 4, pp. 1594–1603, Jul. 2022.
- W. Xu, Z. Yang, D. W. K. Ng, M. Levorato, Y. C. Eldar, and M. Debbah, “Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing,” IEEE J. Sel. Topics Signal Process., vol. 17, no. 1, pp. 9–39, Jan. 2023.
- Z. Zhao, Z. Yang, Y. Hu, L. Lin, and Z. Zhang, “Semantic information extraction for text data with probability graph,” in 2023 IEEE/CIC Int. Conf. Commun. China (ICCC Workshops), Aug. 2023.
- C. Liu, C. Guo, S. Wang, Y. Li, and D. Hu, “Task-oriented semantic communication based on semantic triplets,” in 2023 IEEE Wireless Commun. Netw. Conf. (WCNC), Mar. 2023.
- F. Zhou, Y. Li, X. Zhang, Q. Wu, X. Lei, and R. Q. Hu, “Cognitive semantic communication systems driven by knowledge graph,” in 2022 IEEE Int. Conf. Commun. (ICC), May. 2022, pp. 4860–4865.
- Z. Zhao, Z. Yang, M. Chen, Z. Zhang, and H. V. Poor, “A joint communication and computation design for probabilistic semantic communications,” Entropy, vol. 26, no. 5, Apr. 2024.
- Y. Wang, M. Chen, T. Luo, W. Saad, D. Niyato, H. V. Poor, and S. Cui, “Performance optimization for semantic communications: An attention-based reinforcement learning approach,” IEEE J. Sel. Areas Commun., vol. 40, no. 9, pp. 2598–2613, Jul. 2022.
- D. Wen, K.-J. Jeon, and K. Huang, “Federated dropout—a simple approach for enabling federated learning on resource constrained devices,” IEEE Wireless Commun. Lett., vol. 11, no. 5, pp. 923–927, May 2022.
- Z. Zhao, Z. Yang, X. Gan, Q.-V. Pham, C. Huang, W. Xu, and Z. Zhang, “A joint communication and computation design for semantic wireless communication with probability graph,” Journal of the Franklin Institute, p. 107055, Jul. 2024.