Multiband OFDM UWB Channel Estimation Via a Wavelet Based EM-MAP Algorithm

This paper introduces an expectation-maximization (EM) algorithm within a wavelet domain Bayesian framework for semi-blind channel estimation of multiband OFDM based UWB communications. A prior distribution is chosen for the wavelet coefficients of the unknown channel impulse response, in order to capture the well known sparseness property of the wavelet representation. This prior yields, in maximum a posteriori estimation, a thresholding rule within the EM algorithm. We particularly focus on reducing the number of estimated parameters by iteratively discarding "unsignificant" wavelet coefficients from the estimation process. In addition, the proposed algorithm enhances the estimation accuracy in terms of mean square error with less computational complexity than traditional semi-blind methods. Simulation results using IEEE UWB channel models show that the proposed algorithm outperforms pilot based channel estimation in terms of mean square error and bit error rate