Electric Semantic Compression-Based 6G Wireless Sensing and Communication Integrated Resource Allocation

被引：0

作者：

Liao, Haijun ^{[1
]}

Fan, Jinchao ^{[1
]}

Ci, Haoyu ^{[1
]}

Gu, Jiahua ^{[2
]}

Zhou, Zhenyu ^{[1
]}

Liao, Bin ^{[1
]}

Wang, Xiaoyan ^{[3
]}

Mumtaz, Shahid ^{[4
,5
]}

机构：

[1] North China Electric Power University, School of Electrical and Electronic Engineering, Beijing

[2] State Grid Wuxi Power Supply Company, Jiangsu, Wuxi

[3] Ibaraki University, Graduate School of Science and Engineering, Ibaraki

[4] Nottingham Trent University, Department of Engineering, Nottingham

[5] Kyung Hee University, Department of Electronic Engineering, Gyeonggi, Yongin

来源：

IEEE Internet of Things Journal | 2024年 / 11卷 / 24期

关键词：

6G; distribution grid; fuzzy reinforcement learning; hierarchical collaborative control; integrated sensing and communication; Internet of Things (IoT); Peak Age of Semantics (PAoS);

D O I：

10.1109/JIOT.2024.3444450

中图分类号：

学科分类号：

摘要：

In this article, we address the key problem of sensing and communication integrated resource allocation for 6G-empowered distribution grid hierarchical coordinated control. First, we construct a novel information timeliness metric for electric semantic communication, namely, Peak Age of Semantics (PAoS), which covers the entire lifecycle of information sensing, semantic compression, semantic transmission, and semantic decoding. Second, we propose a sensing and semantic communication integrated resource allocation algorithm based on Top N2 and hybrid knowledge-statistic-driven fuzzy reinforcement learning. A deep fuzzy neural network is utilized to build a knowledge model between the grid operating state and decision making. The knowledge is embedded into statistic-driven model of reinforcement learning to enhance accuracy of upper confidence bound (UCB) utility evaluation. Finally, simulations based on realistic application scenarios indicate that compared with two comparison algorithms, the proposed algorithm reduces average PAoS by 4.72% and 9.49%, and the maximum PAoS by 5.76% and 13.57%. Additionally, its end-to-end delay trend and semantic packet decoding success rate align more closely with semantic importance. © 2024 IEEE.

引用

页码：39333 / 39345

页数：12

共 29 条

[1] Tariq M., Ali M., Naeem F., Poor H.V., Vulnerability assessment of 6G-enabled smart grid cyber-physical systems, IEEE Internet Things J., 8, 7, pp. 5468-5475, (2021)
[2] Sheng H., Wang C., Li B., Liang J., Yang M., Dong Y., Multitimescale active distribution network scheduling considering demand response and user comprehensive satisfaction, IEEE Trans. Ind. Appl., 57, 3, pp. 1995-2005, (2021)
[3] Li Z., Wu L., Xu Y., Wang L., Yang N., Distributed tri-layer riskaverse stochastic game approach for energy trading among multi-energy microgrids, Appl. Energy, 331, (2023)
[4] Tariq M., Adnan M., Srivastava G., Poor H.V., Instability detection and prevention in smart grids under asymmetric faults, IEEE Trans. Ind. Appl., 56, 4, pp. 4510-4520, (2020)
[5] Yang X., Zhou Z., Huang B., URLLC key technologies and Standardization for 6G power Internet of Things, IEEE Commun. Stand. Mag., 5, 2, pp. 52-59, (2021)
[6] Tariq M., Poor H.V., Electricity theft detection and localization in grid-tied microgrids, IEEE Trans. Smart Grid, 9, 3, pp. 1920-1929, (2018)
[7] Al-Quraan M., Et al., Edge-native intelligence for 6G communications driven by federated learning: A survey of trends and challenges, IEEE Trans. Emerg. Topics Comput. Intell., 7, 3, pp. 957-979, (2023)
[8] Zhang S., Yao Z., Liao H., Zhou Z., Chen Y., You Z., Endogenous security-aware resource management for digital twin and 6G edge intelligence integrated smart park, China Commun., 20, 2, pp. 46-60, (2023)
[9] Deng D., Et al., Semantic communication empowered NTN for IoT: Benefits and challenges, IEEE Netw., 38, 4, pp. 32-39, (2024)
[10] Zhou F., Et al., Cognitive semantic communication systems driven by knowledge graph: Principle, implementation, and performance evaluation, IEEE Trans. Commun., 72, 1, pp. 193-208, (2024)

← 1 2 3 →