User Association for Load Balancing in Vehicular Networks: An Online Reinforcement Learning Approach

Recently, a number of technologies have been developed to promote vehicular networks. When vehicles are associated with the heterogeneous base stations (e.g., macrocells, picocells, and femtocells), one of the most important problems is to make load balancing among these base stations. Different from common mobile networks, data traffic in vehicular networks can be observed having regularities in the spatial–temporal dimension due to the periodicity of urban traffic flow. By taking advantage of this feature, the authors propose an online reinforcement learning approach, called ORLA. It is a distributed user association algorithm for network load balancing in vehicular networks. Based on the historical association experiences, ORLA can obtain a good association solution through learning from the dynamic vehicular environment continually. In the long run, the real-time feedback and the regular traffic association patterns both help ORLA cope with the dynamics of network well. In experiments, they use QiangSheng taxi movement to evaluate the performance of ORLA. Their experiments verify that ORLA has higher quality load balancing compared with other popular association methods.

• Record URL:
  https://doi.org/10.1109/TITS.2017.2709462
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2017, IEEE.
• Authors:
  • Li, Zhong
  • Wang, Cheng
  • Jiang, Chang-Jun
• Publication Date: 2017-8

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 2217-2228
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 18
  • Issue Number: 8
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Algorithms; Computer models; Machine learning; Mobile communication systems; Network analysis (Planning); Traffic loads; Vehicle to vehicle communications; Vehicular ad hoc networks
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01644799
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Aug 3 2017 12:00PM