Survey on Machine Learning for Intelligent End-to-End Communication Toward 6G: From Network Access, Routing to Traffic Control and Streaming Adaption

Research output: Contribution to journalReview articlepeer-review

10 Citations (Scopus)

Abstract

The end-to-end quality of service (QoS) and quality of experience (QoE) guarantee is quite important for network optimization. The current 5G and conceived 6G network in the future with ultra high density, bandwidth, mobility and large scale brings urgent requirement of high efficient end-to-end optimization methods. The conventional network optimization methods without learning and intelligent decision ability are hard to handle the high complexity and dynamic scenarios of 6G. Recently, machine learning based QoS and QoE aware network optimization algorithms emerge as a hot research area and attract much attention, which is widely acknowledged as the potential solution for end-to-end optimization in 6G. However, there are still many critical issues of employing machine learning in networks, especially in 6G. In this paper, we give a comprehensive survey on the recent machine learning based network optimization methods to guarantee the end-to-end QoS and QoE. To easy to follow, we introduce the investigated works following the end-to-end transmission flow from network access, routing to network congestion control and adaptive steaming control. Then we discuss some open issues and potential future research directions.

Original languageEnglish
Article number9403380
Pages (from-to)1578-1598
Number of pages21
JournalIEEE Communications Surveys and Tutorials
Volume23
Issue number3
DOIs
Publication statusPublished - 2021 Jul 1
Externally publishedYes

Keywords

  • End-to-end
  • adaptive bitrate streaming (ABR)
  • adaptive streaming control
  • channel assignment
  • congestion control
  • deep learning (DL)
  • machine learning (ML)
  • network access
  • quality of experience (QoE)
  • quality of service (QoS)
  • resource allocation
  • routing

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Survey on Machine Learning for Intelligent End-to-End Communication Toward 6G: From Network Access, Routing to Traffic Control and Streaming Adaption'. Together they form a unique fingerprint.

Cite this