Please submit manuscripts in either of the following two submission systems

    ScholarOne Manuscripts

  • ScholarOne

    • 勤云稿件系统

  • 登录

Search by Issue

  • 2024 Vol.31
  • 2023 Vol.30
  • 2022 Vol.29
  • 2021 Vol.28
  • 2020 Vol.27
  • 2019 Vol.26
  • 2018 Vol.25
  • 2017 Vol.24
  • 2016 vol.23
  • 2015 vol.22
  • 2014 vol.21
  • 2013 vol.20
  • 2012 vol.19
  • 2011 vol.18
  • 2010 vol.17
  • 2009 vol.16
  • No.1
  • No.2

Search by Keywords

  • Search by Title
  • Search by Keywords
  • Search by Abstract
  • Search by Author's Chinese Name
  • Search by Author's English Name
  • Search by Fund

News & AnnouncementMORE

Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

期刊网站二维码
微信公众号二维码
Related citation:Xiaolong Yang,Xuezhi Tan.Estimation for Traffic Arrival Rate and Service Rate of Primary Users in Cognitive Radio Networks[J].Journal of Harbin Institute Of Technology(New Series),2015,22(5):61-68.DOI:10.11916/j.issn.1005-9113.2015.05.010.
【Print】   【HTML】   【PDF download】   【View/Add Comment】  【Download reader】  【 Close 】
←Previous|Next→ Back Issue    Advanced Search
This paper has been: browsed 2064times   downloaded 823times 本文二维码信息
码上扫一扫！
Shared by: Wechat More
Font:larger+|default|smaller-
Estimation for Traffic Arrival Rate and Service Rate of Primary Users in Cognitive Radio Networks
Author NameAffiliation
Xiaolong Yang Communication Research Center, Harbin Institute of Technology, Harbin 150001, China 
Xuezhi Tan Communication Research Center, Harbin Institute of Technology, Harbin 150001, China 
Abstract:
In order to estimate the traffic arrival rate and service rate parameters of primary users in cognitive radio networks, a hidden Markov model estimation algorithm (HMM-EA) is proposed, which can provide better estimation performance than the energy detection estimation algorithm (ED-EA). Firstly, spectrum usage behaviors of primary users are described by establishing a preemptive priority queue model, by which a real state transition probability matrix is derived. Secondly, cooperative detection is utilized to detect the real state of primary users and emission matrix is derived by considering both detection and false alarm probability. Then, a hidden Markov model is built based on the previous two steps, and evaluated through the forward-backward algorithm. Finally, the simulations results verify that the HMM-EA algorithm outperforms the ED-EA in terms of convergence performance, and therefore the secondary user is able to access the unused channel with the least busy probability in real time.
Key words:  cognitive radio  hidden Markov model  cooperative detection
DOI：10.11916/j.issn.1005-9113.2015.05.010
Clc Number:TN929.5
Fund:

LINKS

Copyright © The Editorial Department of Journal of Harbin Institute of Technology
Address: 92 West Dazhi Street, Nan Gang District, Harbin, Heilongjiang Province, China Postcode: 150001
(C)2015 JOURNAL OF HIT ALL RIGHTS RESERVED. ICP. 0000423255