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 Facing the explosively increasing number of multi-service devices, traditional industrial network technology is no longer applicable.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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Bibliographic Details
Published in:IEEE transactions on wireless communications Vol. 22; no. 8; p. 1
Main Authors: Yin, Bo, Tang, Jianhua, Wen, Miaowen
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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 Facing the explosively increasing number of multi-service devices, traditional industrial network technology is no longer applicable.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.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2023.3235748