Low complexity channel models for approximating flat Rayleigh fading in network simulations
This dissertation addresses the need for a computationally efficient fading channel approximation for use in network simulations. A rigorous flat fading channel model was developed for use in accuracy measurements of channel approximations. The popular two-state Markov model channel approximation is analyzed and shown to perform poorly for low to moderate signal-to-noise ratios (SNR). Three novel channel approximations are derived, with multiple methods of parameter estimation. Each model is analyzed for both statistical performance and network performance. The final model is shown to achieve very accurate network throughput performance by achieving a very close matching of the frame run distributions.
This work provides a rigorous evaluation of the popular two-state Markov model, and three novel low complexity channel models in both statistical accuracy and network throughput performance. The novel models are formed through attempts to match key statistical parameters of frame error run and good frame run statistics. It is shown that only matching key parameters is insufficient to achieve an acceptable channel approximation and that it is necessary to approximate the distribution of frame error duration and good frame run duration. The final novel channel approximation, the three-state run-length model, is shown to achieve a good approximation of the desired distributions when some key statistical parameters are matched.
Advisor:Miller, Scott L.; Georghiades, Costas N.; Li, Du; Reddy, A. L. Narasimha
School:Texas A&M University
School Location:USA - Texas
Source Type:Master's Thesis
Keywords:fading channel approximation markov model networking low complexity
Date of Publication:08/01/2003