2012/04/03 by Niels Lovmand Pedersen, Carles Navarro Manchón, Pedersen, Niels Lovmand +6 · 2 citations
Computer Science · Mathematics · #Direction-of-Arrival Estimation Techniques #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Machine Learning (stat.ML) #Statistical Methods and Inference #stat.ML
paper · pdf · doi:10.48550/arxiv.1204.0656
accepted for publication in Proc. IEEE Int Communications (ICC) Conf
arxiv created 2012/04/03 · openalex publication_date 2012/04/03 · arxiv updated 2012/04/04 · openalex created_date 2022/10/02 · openalex updated_date 2026/07/28
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.