Big Data Driven Wireless Communications: A Human-in-the-Loop Pushing Technique for 5G Systems

Qi Yan, Wei Chen, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

The emergence of big data analytics makes it possible to extract and predict user demand from a significant volume of data that are collected from mobile networks. Accurate prediction of user demand based on big data provides the fundamental information for proactive pushing and caching, which can efficiently obtain capacity gains by making full use of idle spectrum when the network is off-peak. In this article, a »human-in-theloop» system combining prediction based on big data analytics with proactive pushing and caching technology is constructed. The key module and some design issues with this system are explained. By taking user demand into account in the humanin-the-loop system, the proposed framework opens up new opportunities in data-driven proactive communication.

Original languageEnglish (US)
Pages (from-to)64-69
Number of pages6
JournalIEEE Wireless Communications
Volume25
Issue number1
DOIs
StatePublished - Feb 2018

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

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