Wireless network analysis and simulations rely on accurate physical layer models.
The increased interest in wireless network design and cross-layer design require an
accurate and efficient physical layer model especially when a large number of nodes
are to be studied and building the real network is not possible. For analysis of upper
layer characteristics, a simplified physical layer model has to be chosen to model the
physical layer.
In this dissertation, the widely used two-state Markov model is examined and
shown to be deficient for low to moderate signal-to-noise ratios. The physical layer
statistics are investigated, and the run length distributions of the good and bad
frames are demonstrated to be the key statistics for accurate physical layer modeling. A four-state Markov model is proposed for the flat Rayleigh fading channel by
approximating the run length distributions with a mixture of exponential distributions. The transition probabilities in the four-state Markov model can be established
analytically without having to run extensive physical layer simulations, which are
required for the two-state Markov model. Physical layer good and bad run length
distributions are compared and it is shown that the four-state Markov model reasonably approximates the run length distributions. Ns2 simulations are performed and
the four-state Markov model provides a much more realistic approximation compared
to the popular two-state Markov model. Achieving good results with the flat Rayleigh fading channel, the proposed four-state Markov model is applied to a few diversity channels. A coded orthogonal fre-
quency division multiplexing (OFDM) system with a frequency selective channel and
the Alamouti multiple-input multiple-output system are chosen to verify the accuracy of the four-state Markov model. The network simulation results show that the
four-state Markov model approximates the physical layer with diversity channel well
whereas the traditional two-state Markov model estimates the network throughput
poorly. The success of adapting the four-state Markov model to the diversity channel
also shows the flexibility of adapting the four-state Markov model to various channel
conditions.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-1785 |
| Date | 02 June 2009 |
| Creators | Yu, Yi |
| Contributors | Miller, Scott L. |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | electronic, application/pdf, born digital |
Page generated in 0.0021 seconds