Joint offloading and streaming in mobile edges: A deep reinforcement learning approach

  • Soohyun Park
  • , Junhui Kim*
  • , Dohyun Kwon
  • , Myungjae Shin
  • , Joongheon Kim
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

This paper proposes a joint dynamic video streaming and deep reinforcement learning (DRL) based offloading method in mobile edge computing systems. In order to utilize video services in mobile edge networks, efficient streaming and offloading algorithms are essentially required. In this paper, therefore, a novel dynamic offloading algorithm is proposed and the algorithm is fundamentally based on deep Q-network (DQN) which is one of widely used deep reinforcement learning algorithms.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728112046
DOIs
Publication statusPublished - 2019 Aug
Externally publishedYes
Event2019 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2019 - Singapore, Singapore
Duration: 2019 Aug 282019 Aug 30

Publication series

NameProceedings - 2019 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2019

Conference

Conference2019 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2019
Country/TerritorySingapore
CitySingapore
Period19/8/2819/8/30

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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