Abstract
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.
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