Physical layer model design for wireless networks

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2009-06-02

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Abstract

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.

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