Energy-Efficient Resource Allocation for Space-Air-Ground Integrated Industrial Power Internet of Things Network

Accompanied by the construction of new power system with renewable energy, like the offshore wind power and desert photovoltaic power, terrestrial ground 5G is no longer able to fulfill the communication requirement of industrial power IoT (IPIoT) with tremendous equipment in remote areas. Under thi...

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Veröffentlicht in:IEEE transactions on industrial informatics Jg. 20; H. 4; S. 5274 - 5284
Hauptverfasser: Qin, Peng, Zhao, Honghao, Fu, Yang, Geng, Suiyan, Chen, Zhiyu, Zhou, Hongxi, Zhao, Xiongwen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.04.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Alternative energy sources
Autonomous aerial vehicles
Continuity (mathematics)
Energy-efficient (EE)
Interference
Internet of Things
Mixed integer
multiple-to-one matching
NOMA
Nonlinear programming
Offshore energy sources
Optimization
Resource allocation
Resource management
Satellites
space–air–ground integrated industrial power IoT (SAGIN-IPIoT)
Task analysis
Uplink
Wind power
ISSN:1551-3203, 1941-0050
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Zusammenfassung:Accompanied by the construction of new power system with renewable energy, like the offshore wind power and desert photovoltaic power, terrestrial ground 5G is no longer able to fulfill the communication requirement of industrial power IoT (IPIoT) with tremendous equipment in remote areas. Under this circumstances, we put forward the NOMA-enabled space-air-ground integrated IPIoT network (SAGIN-IPIoT) model, with a satellite to achieve wide coverage, and multiple UAVs to strengthen hot spot communication. NOMA is leveraged to improve system throughput by allowing the common frequency resource shared among multiple users. An energy-efficient (EE) maximization problem is formulated to jointly optimize subchannel and terminal power. However, since the objective involves both continuous and binary variables, it is a mixed integer nonlinear programming (MINLP) issue. Thus, we decompose it into two subproblems, which are respectively solved by matching game and Lagrange dual method with low complexity. According to the theoretical analysis and simulations, we can conclude that the method has better performance than the benchmark method.
Bibliographie:ObjectType-Article-1
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2023.3331127