Software-Defined Green 5G System for Big Data

Jun Mi, Kun Wang, Peng Li, Song Guo, Yanfei Sun

Research output: Journal article publicationJournal articleAcademic researchpeer-review

18 Citations (Scopus)


The 5G system has been recognized as the most promising technology to provide high-quality network services. As a huge number of networking and computing equipments that generate big data are integrated into the 5G system, energy efficiency becomes the major challenge in building a green 5G system. In this article, we propose a software-defined green 5G system for big data, which consists of three planes: The control plane, the data plane and the energy plane. The data plane contains networking and computing equipments, which can be powered by both traditional grid and renewable energy sources in the energy plane. The control plane monitors the system status and configures the corresponding equipments to achieve energy efficiency and quality-of-service. Furthermore, to reduce the overhead of this software-defined architecture, we investigate a FRS to eliminate redundant system monitoring information. To integrate features in software-defined architecture, we propose an AIFS to mine latent rules among features. Simulation results indicate that our proposals achieve higher efficiency in the green 5G system.

Original languageEnglish
Article number8469815
Pages (from-to)116-123
Number of pages8
JournalIEEE Communications Magazine
Issue number11
Publication statusPublished - 1 Nov 2018

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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