Wavelet Based Semi-blind Channel Estimation For Multiband OFDM

TL;DR

This paper presents a wavelet domain Bayesian EM algorithm for semi-blind channel estimation in multiband OFDM UWB systems, leveraging sparsity to improve accuracy and reduce complexity.

Contribution

It introduces a novel wavelet-based Bayesian EM approach that incorporates sparsity priors and iterative coefficient pruning for efficient semi-blind channel estimation.

Findings

Outperforms pilot-based methods in mean square error and bit error rate.

Reduces computational complexity compared to traditional semi-blind methods.

Effective in sparse channel conditions.

Abstract

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 model a 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. Simulation results using UWB channels issued from both models and measurements show that under sparsity conditions, the proposed algorithm outperforms pilot based channel estimation in terms of mean square error and bit error rate and enhances the estimation accuracy with less computational complexity than traditional semi-blind methods.