Recursive least-squares doubly-selective channel estimation using exponential basis models and subblock-wise tracking

An adaptive channel estimation scheme, exploiting the oversampled complex exponential basis expansion model (CEBEM), is presented for doubly-selective channels where we track the BEM coefficients. We extend/modify the subblockwise tracking method using time-multiplexed (TM) training recently proposed by [1]. Two finite-memory recursive least-squares (RLS) algorithms, including the exponentially-weighted and the sliding-window RLS algorithms, are respectively applied to track the channel BEM coefficients. Simulation examples illustrate the superior performance of our scheme to the conventional block-wise channel estimator, and demonstrate its improvement on our previous work in [1].