Predictive Call Admission Control Algorithm for Power-Controlled Wireless Systems

  • Choong Ming Chin
  • Moh Lim Sim
  • Sverrir Olafsson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4104)


In wireless communication systems, a conventional call admission control (CAC) mechanism determines whether a node can be admitted to the network by firstly monitoring the received interference plus noise and estimate the achievable signal-to-interference-plus-noise ratio (SINR). However, in the presence of power control, the SINR may vary over time, thus, rendering the conventional CAC inaccurate. The maximum achievable SINR for a new node in a general wireless system depends on the link gains amongst all the co-channel interfering nodes involved. Thus, one of the challenges of CAC in a power-controlled wireless system is the estimation of maximum achievable SINR when information about global link gains is not available. By ignoring the white noise factor, we present a predictor for the maximum achievable signal-to-interference ratio (SIR) of a new node trying to gain access to the medium. Using the SIR predictor we then calculate an optimal active link protection margin, which together with a SIR threshold would constitute an enhanced threshold value for the new node to attain. By doing so current active communication links would be protected from performance degradation should the maximum achievable SIR value common to all the nodes be lower than the SIR threshold. The accuracy of the predictor is evaluated by means of simulation in terms of mean error and root-mean-square error. Together with finding the corresponding optimal active link protection margin, efficient CAC mechanism to ensure stability of the feasible system can be maintained over a wide range of operating SIR values.


Power Control Mobile Station Active Link False Rejection Distribute Power Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Choong Ming Chin
    • 1
  • Moh Lim Sim
    • 2
  • Sverrir Olafsson
    • 3
  1. 1.Asian Research CentreBT GroupCyberjayaMalaysia
  2. 2.Multimedia UniversityCyberjayaMalaysia
  3. 3.Mobility Research CentreBT GroupIpswichUK

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