On the Optimum Number of Hypotheses for Adaptive Reduced-Rank Subspace Selection

Intelligent transport systems (ITS) require low-latency dependable wireless communication links inbetween vehicles as well as between vehicles and the infrastructure. In vehicular communication scenarios communication channels are time-and frequency (doubly) dispersive and the channel statistics are non-stationary, i.e., they change over time. Hence, the design of appropriate channel estimators is challenging. Recently an adaptive reduced-rank channel estimation technique for non-stationary time-variant channel estimation was introduced by Zemen and Molisch, 2012. This technique uses a hypothesis test to obtain an estimate of the current channel statistics on a per frame basis. The optimum number of hypotheses is not known. In this paper we present new empirical insights on the optimum choice of the number of hypotheses for the hypothesis test for non-stationary time-variant channel estimation. With these considerations the complexity of an adaptive reduced-rank channel estimator can be reduced and its performance improved.