Heterogeneous RAN slicing resource allocation using mathematical program with equilibrium constraints

Network slicing is considered to be a key feature of the 5th generation mobile networks. It permits multiple tenants, i.e. mobile virtual network operators, to share virtual resources. However, each tenant only considers the individual slice utility, which leads to unfair resource allocation among t...

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Bibliographic Details
Published in:IET communications Vol. 16; no. 15; pp. 1772 - 1786
Main Authors: Ma, Tengteng, Zhang, Yong, Han, Zhu, Li, Chen
Format: Journal Article
Language:English
Published: Stevenage John Wiley & Sons, Inc 01.09.2022
Wiley
Subjects:
Algorithms
Constraints
Deep learning
Distance learning
Equilibrium
Heuristic
Infrastructure
Linear programming
Markov analysis
Network slicing
Operators (mathematics)
Quality of service
Resource allocation
Teaching methods
Utilities
Virtual networks
ISSN:1751-8628, 1751-8636
Online Access:Get full text
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Summary:Network slicing is considered to be a key feature of the 5th generation mobile networks. It permits multiple tenants, i.e. mobile virtual network operators, to share virtual resources. However, each tenant only considers the individual slice utility, which leads to unfair resource allocation among tenants. To achieve the aim that the infrastructure provider can fairly allocate virtual resources to tenants, a two‐layer resource allocation architecture in a heterogeneous radio access network (RAN) is proposed and it is formulated as a mathematical program with equilibrium constraints (MPEC). The existence of the solution in the lower layer is proved via the properties of the quasi‐variational problem, indicating that the MPEC is solvable. Combining the two‐layer architecture and successive convex approximation method, a fair algorithm is proposed, which provides fair resource allocation strategies for the infrastructure provider. Compared with the existing static slicing and social optimal methods, the analysis and simulation results confirm that the proposed algorithm weighs the utilities of the total network system and each tenant. In addition, regarding their utilities, the gap between the proposed method and the social optimal is within 5%, which outperforms static slicing.
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ISSN:1751-8628
1751-8636
DOI:10.1049/cmu2.12423