Connectivity Maximization in Non-orthogonal Network Slicing Enabled Industrial Internet-of-Things with Multiple Services

Industrial Internet of Things (IIoT) is a technological revolution that is profoundly reshaping the visage of industry. Facing the explosively increasing number of multi-service devices, traditional industrial network technology is no longer applicable. The advent of the fifth-generation (5G) wirele...

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Vydané v:IEEE transactions on wireless communications Ročník 22; číslo 8; s. 1
Hlavní autori: Yin, Bo, Tang, Jianhua, Wen, Miaowen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
5G mobile communication
Algorithms
Complexity
Connectivity maximization
Density
Downlink
Industrial applications
Industrial Internet of Things
Industrial Internet of Things (IIoT)
Integer programming
Linear programming
Maximization
Mixed integer
Network slicing
NOMA
non-orthogonal multiple access (NOMA)
Nonlinear programming
Nonorthogonal multiple access
Optimization
resource allocation
Resource management
Uplink
Wireless networks
ISSN:1536-1276, 1558-2248
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Popis
Shrnutí:Industrial Internet of Things (IIoT) is a technological revolution that is profoundly reshaping the visage of industry. Facing the explosively increasing number of multi-service devices, traditional industrial network technology is no longer applicable. The advent of the fifth-generation (5G) wireless networks brings unprecedented possibilities for deploying the anticipated IIoT. To address the two main issues, i.e., connection density and multi-service requirements, in 5G empowered IIoT, we consider the non-orthogonal network slicing in this work. In particular, we jointly utilize network slicing to incorporate different types of services and exploit non-orthogonal multiple access (NOMA) to enhance the connection density. We formulate the connectivity maximization problem with joint sub-carrier association and power allocation as a mixed-integer nonlinear programming (MINLP). To tackle the intractable MINLP, we first transform it into a mixed-integer linear programming (MILP) and then simplify the MILP into an integer linear programming (ILP) by developing a simple yet effective pairing guideline. In order to further reduce the computational complexity, we then propose the alternating selection best-effort pairing (AS-BEP) algorithm with low complexity to solve the ILP effectively. Our analyses are supplemented by comprehensive simulation results that illustrate the performance superiority of the proposed algorithms to the benchmark schemes.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2023.3235748