A GAMP Based Low Complexity Sparse Bayesian Learning Algorithm

8 Mar 2017Maher Al-ShoukairiPhilip SchniterBhaskar D. Rao

In this paper, we present an algorithm for the sparse signal recovery problem that incorporates damped Gaussian generalized approximate message passing (GGAMP) into Expectation-Maximization (EM)-based sparse Bayesian learning (SBL). In particular, GGAMP is used to implement the E-step in SBL in place of matrix inversion, leveraging the fact that GGAMP is guaranteed to converge with appropriate damping... (read more)

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