Physical layer model design for wireless networks

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

Modeling of multipath fading channels for network simulation

Development of accurate physical layer models is very important for generating realistic network simulation results. Significant effort has been put into setting up physical layer models for wireless channels that emulate the impact of the channel on the higher layers of the network. Setting up the models is especially difficult for a frequency selective channel. In this thesis the use of non-linear functions to convert the frequency selective channel to an equivalent flat fading channel is examined. The analytical expressions for the statistics of the equivalent flat fading process that are needed to set up the physical layer models are derived. These results are used to set up the physical layer model for the frequency selective channel. Extensive simulations are performed to verify the accuracy of the model against a detailed physical layer implementation. The statistics of the model and the actual channel are seen to match, validating the method of setting up the models