Enhanced tracking of radio propagation path parameters using state-space modeling

Future wireless communication systems will exploit the rich spatial and temporal diversity of the radio propagation environment. This requires new advanced channel models, which need to be verified by real-world channel sounding measurements. In this context the reliable estimation and tracking of the model parameters from measurement data is of particular interest. In this paper, we build a state-space model, and track the propagation parameters with the Extended Kalman Filter in order to capture the dynamics of the channel parameters in time. We then extend the model by considering first order derivatives of the geometrical parameters, which enhances the tracking performance due to improved prediction and robustness against shadowing and fading. The model also includes the effect of distributed diffuse scattering in radio channels. The issue of varying state variable dimension, i.e., the number of propagation paths to track, is also addressed. The performance of the proposed algorithms is demonstrated using both simulated and measured data.