GAN-Based Pareto Optimization for Self-Healing of Radio Access Network Slices

Radio Access Network (RAN) slicing is a promising architectural technology to address extremely diversified service demands and provide profitable business models for future mobile networks. In RAN slicing architecture, self-healing is an important functional module to minimize the impact of network...

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Bibliographic Details
Published in:IEEE eTransactions on network and service management 2022-03, Vol.19 (1), p.146-157
Main Authors: Wang, Yatong, Qin, Shuang, Feng, Gang, Zhou, Jianhong, Wei, Fengsheng
Format: Article
Language:English
Subjects:
Convergence
Evolutionary algorithms
generative adversarial network
Generative adversarial networks
Indium phosphide
Maintenance
Maintenance engineering
Modules
Optimization
Pareto optimization
radio access network slice
Radio access networks
Self-healing
Slicing
Utilities
Wireless communication
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Summary:Radio Access Network (RAN) slicing is a promising architectural technology to address extremely diversified service demands and provide profitable business models for future mobile networks. In RAN slicing architecture, self-healing is an important functional module to minimize the impact of network failings on the performance of RAN slices. However, self-healing of burgeoning sliced RAN is vastly different from that of traditional RAN and has been rarely investigated. In this paper, we address the Self-healing of RAN Slice (SRANS) problem by modeling it as a Pareto optimization problem with the aim of maximizing the self-healing utilities of individual RAN slices. To deal with the weakness of diversity maintenance in traditional Pareto optimization methods, we propose a Generative Adversarial Network (GAN) based Pareto Optimization (GPO) framework. Specifically, we employ self-conditioned GANs to replace the offspring reproduction module in the traditional Evolutionary Algorithm (EA), where the insufficiency of diversity maintenance in EAs is effectively overcome. Furthermore, we theoretically prove that GPO framework is guaranteed to converge to the optimal Pareto solution set. Numerical results demonstrate that the convergence of proposed GPO framework can be expedited by enhancing the diversity of solution sets in solving the SRANS problem. Compared with traditional schemes, GPO can achieve significant performance gain in terms of the utilities and isolation level of repaired RAN slices.
ISSN:1932-4537
1932-4537
DOI:10.1109/TNSM.2021.3120259