Bayesian sequential learning for railway cognitive radio

Applying cognitive radio in the railway communication systems is a cutting-edge research area. The rapid motion of the train makes the spectrum access of the railway wireless environment instable. To address the issue, first the authors formulate the spectrum management of railway cognitive radio as a distributed sequential decision problem. Then, based on the available environmental information, the authors propose a multi-cognitive-base-station cascade collaboration algorithm by using naive Bayesian learning and agent theory. Finally, the authors' experiment results reveal that the model can improve the performance of spectrum access. This cognitive-base-station multi-agent system scheme comprehensively solves the problem of low efficiency in the dynamic access of the railway cognitive radio. The article is also a typical case of artificial intelligence applied in the field of the smart city.

• Record URL:
  • https://doi.org/10.7307/ptt.v31i2.2934
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03535320
• Supplemental Notes:
  • © 2019 Cheng Wang et al.
• Authors:
  • Wang, Cheng
  • Wang, Yiming
  • Wu, Cheng
• Publication Date: 2019

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 141-149
• Serial:
  • PROMET-Traffic & Transportation
  • Volume: 31
  • Issue Number: 2
  • Publisher: University of Zagreb
  • ISSN: 0353-5320
  • EISSN: 1848-4069
  • Serial URL: https://traffic2.fpz.hr/index.php/PROMTT
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Bayes' theorem; Electromagnetic spectrum; Machine learning; Radio; Railroads; Wireless communication systems
• Subject Areas: Data and Information Technology; Railroads;

Filing Info

• Accession Number: 01712431
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 24 2019 10:36PM