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

An adaptive channel estimation scheme, exploiting the over- sampled complex exponential basis expansion model (CE- BEM), is presented for doubly-selective channels where we track the BEM coefficients. We extend/modify the subblock- wise tracking method using time-multiplexed (TM) training recently proposed by S. He and J. K. Tugnait (2007). 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.