Cluster spreads for time-variant vehicular channels

Cluster based channel models can be used to reduce the computational complexity. For vehicular communications, the environment changes rapidly due to the high velocities of the transmitter and receiver, resulting in fast changing cluster parameters. In this paper, we present an automatic cluster identification and tracking algorithm in order to consistently characterize the evolution of cluster parameters, e.g. delay and Doppler spreads. We apply the algorithm to a set of vehicular channel measurements. By analyzing the time-variant spreads of clusters, we find that the cluster associated with line-of-sight (LOS) components usually also consists of multipath components coming from several other objects, thus resulting in more dynamically changing spreads. Clusters at high Doppler shifts, stemming from big vehicle driving in the opposite direction, exhibit stable spreads. We apply the algorithm to a set of vehicular channel measurements and provide the fitting parameters.