Multiple-measurement vector based implementation for single-measurement vector sparse Bayesian learning with reduced complexity

Publication Type:
Journal Article
Signal Processing, 2016, 118 pp. 153 - 158
Issue Date:
Filename Description Size
1-s2.0-S0165168415002194-main.pdfPublished Version347.58 kB
Adobe PDF
Full metadata record
© 2015 Elsevier B.V. Abstract Sparse Bayesian learning (SBL) has high computational complexity associated with matrix inversion in each iteration. In this paper, we investigate complexity reduced multiple-measurement vector (MMV) based implementation for single-measurement vector SBL problems. For problems with special structured sensing matrices, we propose two sub-optimal SBL schemes with significantly reduced complexity and slight estimation performance degradation, by exploiting the deterministic correlation in the converted MMV model explicitly. Two application scenarios on channel estimation in multicarrier systems and direction of arrival estimation are presented. Simulation results validate the effectiveness of the schemes.
Please use this identifier to cite or link to this item: