Congestion prevention in broadband wireless access systems: An economic approach

  • Authors:

  • Bader Al-Manthari;Nidal Nasser;Najah Abu Ali;Hossam Hassanein

  • Affiliations:

  • Center of Information Security, Information Technology Authority, Azaibah, P.O. Box: 1807, P.C: 130, Sultanate of Oman;School of Science, University of Guelph, Guelph, ON, Canada N1G 2W1;College of Information Technology, UAE University, Al-Ain, P.O. 17555, UAE;Telecommunications Research Laboratory, School of Computing, Queen's University, Kingston, ON, Canada K7L 3N6

  • Venue:
  • Journal of Network and Computer Applications
  • Year:
  • 2011

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Abstract

While the demand for mobile broadband wireless services continues to increase, radio resources remain scarce. Even with the substantial increase in the supported bandwidth in the next generation broadband wireless access systems (BWASs), it is expected that these systems will severely suffer from congestion, due to the rapid increase in demand of bandwidth-intensive multimedia services. Without efficient bandwidth management and congestion control schemes, network operators may not be able to meet the increasing demand of users for multimedia services, and hence they may suffer an immense revenue loss. In this paper, we propose an admission-level bandwidth management scheme consisting of call admission control (CAC) and dynamic pricing. The main aim of our proposed scheme is to provide monetary incentives to users to use the wireless resources efficiently and rationally, hence, allowing efficient bandwidth management at the admission level. By dynamically determining the prices of units of bandwidth, the proposed scheme can guarantee that the number of connection requests to the system are less than or equal to certain optimal values computed dynamically, hence, ensuring a congestion-free system. The proposed scheme is general and can accommodate different objective functions for the admission control as well as different pricing functions. Comprehensive simulation results with accurate and inaccurate demand modeling are provided to show the effectiveness and strengths of our proposed approach.