Application of Bayesian Hierarchical Prior Modeling to Sparse Channel Estimation

Existing methods for sparse channel estimation typically provide an estimate computed as the solution maximizing an objective function defined as the sum of the log-likelihood function and a penalization term proportional to the L1-norm of the parameter of interest. However, other penalization terms have proven to have strong sparsity-inducing properties. In this paper, the authors design pilot-assisted channel estimators for OFDM wireless receivers within the framework of sparse Bayesian learning by defining hierarchical Bayesian prior models that lead to sparsity-inducing penalization terms.