Application of Bayesian Hierarchical Prior Modeling to Sparse Channel Estimation

Autor:	Pedersen, Niels Lovmand, Manchón, Carles Navarro, Shutin, Dmitriy, Fleury, Bernard Henri
Rok vydání:	2012
Předmět:	Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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 work, we 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. The estimators result as an application of the variational message-passing algorithm on the factor graph representing the signal model extended with the hierarchical prior models. Numerical results demonstrate the superior performance of our channel estimators as compared to traditional and state-of-the-art sparse methods. Comment: accepted for publication in Proc. IEEE Int Communications (ICC) Conf
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1204.0656 Zobrazit plný text záznamu View this record from Arxiv