skip to main content
10.1145/2739480.2754663acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
research-article

A New Perspective on Channel Allocation in WLAN: Considering the Total Marginal Utility of the Connections for the Users

Published:11 July 2015Publication History

ABSTRACT

The channel allocation problem consists in defining the frequency used by Access Points (APs) in Wireless Local Area Networks (WLAN). An overlap of channels in a WLAN is the major factor of performance reduction for the users in a network. For this reason, we propose a new model for channel allocation that aims to maximize the total quality of the connection of the user by considering their marginal utility. The results show that an allocation model that does not take into account the total utility of each connection tends to prioritize the quality of connection of a few users and lead to a large unbalance in the distribution of connection speed between users. Thus, the new model can handle the importance of degradation caused by the levels of interference in the user connection separately.

References

  1. R. Akl and A. Arepally. Dynamic channel assignment in ieee 802.11 networks. IEEE International Conference on Portable Information Devices, pages 1--5, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  2. S. J. Bae, B. G. Choi, and H. S. Chae. Self-configuration scheme to alleviate interference among aps in ieee 802.11 wlan. The 23rd IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), pages 1025--1030, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  3. H. Balbi, N. Fernandes, F. Souza, R. Carrano, C. Albuquerque, D. Muchaluat-Saade, and L. Magalhaes. Centralized channel allocation algorithm for ieee 802.11 networks. Global Information Infrastructure and Networking Symposium (GIIS), pages 1--7, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  4. D. Brélaz. New methods to color the vertices of a graph. Communications of the ACM, 22:251--256, 1979. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. M. Drieburg, F.-C. Zheng, R. Ahmad, and S. Olafsson. An improved distributed dynamic channel assignment scheme for dense wlans. Proc. of the 6th International Conference on Information, Communications and Signal Processing (ICICS), pages 1--5, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  6. A. E. Eiben and J. E. Smith. Introduction to Evolutionary Computing. Natural Computing. Springer, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. R. Frank and B. Bernanke. Principles of Economics. McGraw-Hill Irwin, 4 edition, 2009.Google ScholarGoogle Scholar
  8. D. Gong, M. Zhao, and Y. Yang. Distributed channel assignment algorithms for 802.11n wlans with heterogeneous clients. In Journal of Parallel and Distributed Computing, 74:2365--2379, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Handrizal, M. Zarlis, A. Noraziah, and A. N. Abdalla. An improved of channel allocation for wlan using vertex merge algorithm. International Conference on Computational Science and Information Management (ICoCSIM), 1:205--213, 2012.Google ScholarGoogle Scholar
  10. A. Hills. Large-scale wireless lan design. IEEE Communications Magazine, 39:98--107, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. A. Hills and J. Schlegel. Rollabout: A wireless design tool. IEEE Communications Magazine, 42:132--138, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. P. Lima, E. G. Carrano, and R. H. C. Takahashi. Multiobjective planning networks wlan using genetic algorithms. WCCI IEEE Congress on Computational Intelligence, 2012.Google ScholarGoogle Scholar
  13. H. Luo and N. K. Shankaranarayanan. A distributed dynamic channel allocation technique for throughput improvement in a dense wlan environment. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 5:345--348, 2004.Google ScholarGoogle Scholar
  14. J. MacQueen. Some methods of classification and analysis of multivariate observations. Proc. of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1:281--297, 1967.Google ScholarGoogle Scholar
  15. P. Mahonen, J. Riihijarvi, and M. Petrova. Frequency allocation for wlans using graph colouring techniques. Proc. WONS'05, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. S. Manitpornsut, B. Landfeldt, and A. Boukerche. Efficient channel assignment algorithms for infrastructure wlans under dense deployment. Proc. of the 12th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pages 329--337, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. N. G. Mankiw. Principles of Economics. Cengage Learning, 6 edition, 2011.Google ScholarGoogle Scholar
  18. B. H. Park, S. J. Bea, and Y. M. Kwon. Connection management of mobile nodes for transmission power control in wlan aps. International Conference on ICT Convergence, pages 770--771, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  19. K. M. Ramachandran and C. P. Tsokos. Mathematical Statistics with Applications in R. 2 edition, 2014.Google ScholarGoogle Scholar
  20. T. Vanhatupa, M. Hannikainen, and T. D. Hamalainen. Evaluation of throughput estimation models and algorithms for wlan frequency planning. Computer Networks, 51:3110--3124, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. T. Yao, X. Guo, Y. Qiu, and L. Ge. An integral optimization framework for wlan design. International Conference on Communication Technology (ICCT), pages 360--365, 2013.Google ScholarGoogle Scholar

Index Terms

  1. A New Perspective on Channel Allocation in WLAN: Considering the Total Marginal Utility of the Connections for the Users

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation
      July 2015
      1496 pages
      ISBN:9781450334723
      DOI:10.1145/2739480

      Copyright © 2015 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 11 July 2015

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      GECCO '15 Paper Acceptance Rate182of505submissions,36%Overall Acceptance Rate1,669of4,410submissions,38%

      Upcoming Conference

      GECCO '24
      Genetic and Evolutionary Computation Conference
      July 14 - 18, 2024
      Melbourne , VIC , Australia

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader