10:50 - 11:10 | Fri 17 Mar | Main Room | S6.2
Bayesian Approximate Message Passing (BAMP) provides excellent recovery performance in Compressed Sensing (CS), but it seemingly needs to know the pdf of the signal prior. If the shape of the pdf is known but not its parameters we show in this work how those parameters can be estimated with very low complexity during the BAMP iterations by the well-known Method of Moments. We compare the new approach with an established scheme from the literature that is based on the Expectation Maximization (EM) algorithm. By simulations we show that the MoM-based BAMP scheme works at least as good as the EM-based approach, but at much lower complexity.