TY - GEN
T1 - Application of Bayesian Hierarchical Prior Modeling to Sparse Channel Estimation
AU - Pedersen, Niels Lovmand
AU - Manchón, Carles Navarro
AU - Shutin, Dmitriy
AU - Fleury, Bernard Henri
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84871987024&partnerID=8YFLogxK
U2 - 10.1109/ICC.2012.6363847
DO - 10.1109/ICC.2012.6363847
M3 - Article in proceeding
SN - 978-1-4577-2052-9
T3 - I E E E International Conference on Communications
SP - 3487
EP - 3492
BT - 2012 IEEE International Conference on Communications (ICC)
T2 - 2012 IEEE International Conference on Communications
Y2 - 10 June 2012 through 15 June 2012
ER -