Stochastic UWB wireless channel modeling and estimation from received signal measurements

In this paper, stochastic differential equations (SDEs) are used to model ultra-wideband (UWB) indoor channels. We show that the impulse responses for time-varying wireless channels can be approximated in a mean square sense as close as desired by impulse responses that can be realized by SDEs. The Expected Maximization and Extended Kalman Filter are employed to recursively identify and estimate the channel parameters and states, respectively, from online received signal measurement data. The state variables represent the inphase and quadrature components of the UWB channel. Both resolvable and non-resolvable multipath received signals are considered and represented as small-scaled Nakagami fading. The proposed models together with the estimation algorithm are tested using UWB indoor measurement data and the results are presented.