Doubly-Selective Channel Estimation Using Exponential Basis Models and Subblock Tracking

We present a novel approach to doubly-selective channel estimation exploiting the complex exponential basis expansion model (CE-BEM) for the overall time-variant channel and an autoregressive (AR) model for the BEM coefficients. Since the time-varying nature of the channel is well captured in CE-BEM by the known exponential basis functions, the time variation of the (unknown) BEM coefficients is likely much slower than that of the channel. We propose a novel "subblock- wise" BEM coefficient tracking scheme based on Kalman filtering and time-multiplexed periodically transmitted training symbols. Simulation examples demonstrate its superior performance over several existing doubly-selective channel estimators.