1st International ICST Workshop on Technology and Policy for Accessing Spectrum

Research Article

Dynamic spectrum access in WLAN channels: empirical model and its stochastic analysis

  • @INPROCEEDINGS{10.1145/1234388.1234402,
        author={Stefan Geirhofer and Lang  Tong and Brian M.  Sadler},
        title={Dynamic spectrum access in WLAN channels: empirical model and its stochastic analysis},
        proceedings={1st International ICST Workshop on Technology and Policy for Accessing Spectrum},
        publisher={ACM},
        proceedings_a={TAPAS},
        year={2006},
        month={8},
        keywords={WLAN Modeling Dynamic Spectrum Access Coexistence. ∗Prepared though collaborative participation in the Communications},
        doi={10.1145/1234388.1234402}
    }
    
  • Stefan Geirhofer
    Lang Tong
    Brian M. Sadler
    Year: 2006
    Dynamic spectrum access in WLAN channels: empirical model and its stochastic analysis
    TAPAS
    ACM
    DOI: 10.1145/1234388.1234402
Stefan Geirhofer1,*, Lang Tong1,*, Brian M. Sadler2,*
  • 1: School of Electrical and Computer Engineering, Cornell University, Ithaca, NY.
  • 2: Army Research Laboratory, Adelphi.
*Contact email: sg355@cornell.edu, lt35@cornell.edu, bsadler@arl.army.mil

Abstract

In this work we are concerned with dynamically sharing the spectrum in the time-domain by exploiting whitespace between the bursty transmissions of a primary user, represented by an 802.11b-based wireless LAN (WLAN). For deriving such schemes we need to establish a model of the WLAN's medium access as to predict its behavior accurately. Moreover, a balance between accuracy and complexity needs to be struck as to render the model useful in practice. We emphasize that our model is based on actual measurements at 2.4GHz using a vector signal analyzer.

We have shown previously that a semi-Markov model is a viable approach for modeling the busy/idle durations. In the present paper we extend our results by (i) expanding the measurement setup and looking at more realistic traffic scenarios, (ii) providing a better approximation to the distribution of the idle durations, and (iii) fitting a phasetype approximation to arrive at a computationally simpler description. The goodness-of-fit of the proposed models is evaluated using the Kolmogorov-Smirnov test.