Application of Bayesian Hierarchical Prior Modeling to Sparse Channel Estimation
Source: Aalborg University
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.