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Adv. Signal Process."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>New services, such as distributed photovoltaic regulation and control, pose new service requirements for communication networks in the new power system. These requirements include low latency, high reliability, and large bandwidth. Consequently, power heterogeneous communication networks face the challenge of maintaining quality of service (QoS) while enhancing network resource utilization. Therefore, this paper puts forward a highly efficient optimization algorithm for resource slicing and scheduling in power heterogeneous communication networks. Our first step involves establishing an architectural description model of heterogeneous wireless networks for electric power based on hypergraph. This model characterizes complex dynamic relationships among service requirements, virtual networks, and physical networks. The system congruence entropy characterizes the degree of matching between the service demand and resource supply. Then an optimization problem is formed to maximize the system congruence entropy through dynamic resource allocation. To solve this problem, a joint resource allocation and routing method based on Lagrangian dual decomposition is proposed. These methods provide the optimal solutions of the nodes and link mappings of service function chains. The simulation results demonstrate that the proposed algorithm in this paper can greatly enhance resource utilization and also meet the QoS requirements of various services.<\/jats:p>","DOI":"10.1186\/s13634-024-01135-1","type":"journal-article","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T04:24:27Z","timestamp":1711081467000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A highly efficient resource slicing and scheduling optimization algorithm for power heterogeneous communication networks based on hypergraph and congruence entropy"],"prefix":"10.1186","volume":"2024","author":[{"given":"Wendi","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengling","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linqing","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongxu","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,22]]},"reference":[{"key":"1135_CR1","unstructured":"T.L. Ren, Research on 5G Network Slicing Resource Management Algorithm for Power Communication (North China Electric Power University, 2022)"},{"key":"1135_CR2","first-page":"1","volume":"45","author":"Y Gao","year":"2021","unstructured":"Y. Gao, Z. Zhang, H. Lin, X. Zhao, S. Du, C. Zou, Hypergraph learning: methods and practices. IEEE Trans. Pattern Anal. Mach. Intell. 45, 1\u20131 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1135_CR3","unstructured":"X.L. Wang, Research on Virtual Resource Management Technology for 5G Network Slicing (Information Engineering University of the Strategic Support Force, 2019)"},{"key":"1135_CR4","doi-asserted-by":"crossref","unstructured":"O.A. Latif, M. Amer, A. 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Mag. 55(12), 70\u201376 (2017)","journal-title":"IEEE Commun. Mag."},{"issue":"01","key":"1135_CR7","first-page":"26","volume":"42","author":"YH Zang","year":"2023","unstructured":"Y.H. Zang, H.K. Zheng, S.H. Yin, Resource allocation strategy for 5G network slicing oriented to new power systems. Hebei Electr. Power Technol. 42(01), 26\u201331 (2023)","journal-title":"Hebei Electr. Power Technol."},{"issue":"07","key":"1135_CR8","first-page":"189","volume":"42","author":"J Huang","year":"2021","unstructured":"J. Huang, F. Yang, Y.Z. Xie, Spectrum sharing strategy for network slicing in cognitive capacity collection networks. J. Commun. 42(07), 189\u2013197 (2021)","journal-title":"J. Commun."},{"issue":"06","key":"1135_CR9","first-page":"22","volume":"37","author":"JL Wang","year":"2021","unstructured":"J.L. Wang, Virtual network resource allocation algorithm based on genetic algorithm under network slicing. Jiangsu Commun. 37(06), 22\u201325 (2021)","journal-title":"Jiangsu Commun."},{"key":"1135_CR10","unstructured":"G.F. Zhao, Research on Reliability-oriented 5G Network Slicing Mapping Algorithm (Chongqing University of Posts and Telecommunications, 2020)"},{"key":"1135_CR11","unstructured":"M.Y. Liu, Research on 5G Network Slicing Resource Allocation Algorithm for Smart Grids (North China Electric Power University, Beijing, 2022)"},{"key":"1135_CR12","doi-asserted-by":"crossref","unstructured":"C. Song, C. Ma, Distributed multi-node slicing model and application analysis for power information collection scene, in 2022 China International Conference on Electricity Distribution (CICED), Changsha, China (2022), pp. 1222\u20131227","DOI":"10.1109\/CICED56215.2022.9928895"},{"key":"1135_CR13","doi-asserted-by":"crossref","unstructured":"Y. Zhao, J. Shen, X. Qi, X. Wang, X. Zhao, J. 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