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Heterogeneous Network Selection Algorithm Based on Reinforcement Learning

  • Harbin Institute of Technology
  • Ministry of Public Security of the People's Republic of China

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In the current network environment, multiple wireless access technologies coexist. In order to meet the needs of various services in heterogeneous networks, in order to enable each user to select the most appropriate network to provide services and adapt to the dynamic changes of the network environment, this paper defines the markov decision-making process of network selection based on reinforcement learning, taking the heterogeneous network constructed by PDT and B-trunC as the background A network access control algorithm based on reinforcement learning is proposed, which fully considers the service type of session and the mobility of terminal, and realizes the adaptability of network selection.

Original languageEnglish
Title of host publicationCommunications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems
EditorsQilian Liang, Wei Wang, Xin Liu, Zhenyu Na, Xiaoxia Li, Baoju Zhang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages56-63
Number of pages8
ISBN (Print)9789811584107
DOIs
StatePublished - 2021
Event9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020 - Changbaishan, China
Duration: 4 Jul 20205 Jul 2020

Publication series

NameLecture Notes in Electrical Engineering
Volume654 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020
Country/TerritoryChina
CityChangbaishan
Period4/07/205/07/20

Keywords

  • Heterogeneous network
  • Mobility
  • Reinforcement learning

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