A statistical approach to developing channel models for network simulations

In this paper, we investigate the development of channel models for wireless network simulations based upon distributions of a representative frame error process. Our approach utilizes the distributions of 1) good frame run lengths and 2) bad frame run lengths as a metric for developing low complexity channel models. Two unique, low complexity flat Rayleigh fading channel approximations are generated and compared against the traditional two-state Markov approximation. We quantify the performance of channel approximations through ns2 network simulations. This work furthers the understanding of channel model development by proposing a metric by which to design and evaluate channel approximations. In addition, this metric is evaluated for the case of flat Rayleigh fading.