The Modeling Method of Time-Correlated MIMO Channels Using the Particle Filter

The ray-based channel models with parameters derived from measured data such as the 3GPP Spatial Channel Model has been widely used as the standard for evaluating the performance of MIMO transmission technologies used in next generation cellular systems such as LTE and IMT-Advanced. Recently, for evaluating the temporal evolution of MIMO transmission performance, demand has been rising for enhanced channel models that include not only the static channel behavior but also dynamic and time-variant channel behavior. In this paper, we propose a Dynamic Channel Model. In our model, we estimate the dynamic properties of rays such as the ray´s birth time, death time, and propagation parameter drift using Rao-Blackwellized particle filter. Experiments show that our dynamic channel model yields AoA, ToA, and AoD attributes, spread and auto-correlation, that agree with data measured in an urban environment well.