The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Expectation-Maximization Gaussian-Mixture Approximate Message Passing
Open accessAbstract
When recovering a sparse signal from noisy compressive linear measurements, the distribution of the signal's non-zero coefficients can have a profound effect on recovery mean-squared error (MSE). If this distribution was a priori known, then one could use computationally efficient approximate message passing (AMP) techniques for nearly minimum MSE (MMSE) recovery. In practice, however, the distribution is unknown, motivating the use of robust algorithms like LASSO-which is nearly minimax optimal-at the cost of significantly larger MSE for non-least-favorable distributions. As an alternative, we propose an empirical-Bayesian technique that simultaneously learns the signal distribution while MMSE-recovering the signal-according to the learned distribution-using AMP. In particular, we model the non-zero distribution as a Gaussian mixture and learn its parameters through expectation maximization, using AMP to implement the expectation step. Numerical experiments on a wide range of signal classes confirm the state-of-the-art performance of our approach, in both reconstruction error and runtime, in the high-dimensional regime, for most (but not all) sensing operators.
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Cites
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Cited by
Compressed Channel Estimation for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems
Wang, P., Fang, J., Duan, H., & Li, H. (2020) · IEEE Signal Processing Letters · doi:10.1109/lsp.2020.2998357 · Cited by 530
AMP-Inspired Deep Networks for Sparse Linear Inverse Problems
Borgerding, M., Schniter, P., & Rangan, S. (2017) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2017.2708040 · Cited by 455
Self-citationKe, M., Gao, Z., Wu, Y., Gao, X., & Schober, R. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.2967175 · Cited by 417
Channel Estimation in Broadband Millimeter Wave MIMO Systems With Few-Bit ADCs
Mo, J., Schniter, P., & Heath, R. W. (2018) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2017.2781644 · Cited by 348
Self-citationZhang, Z., Jung, T.-P., Makeig, S., & Rao, B. D. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2226175 · Cited by 340
Metadata
- Authors
- Vila, J. P., & Schniter, P.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2013-10
- Volume
- 61
- Issue
- 19
- Pages
- 4658-4672
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2026933032
- Cited by
- 501
- References
- 37
- FWCI
- 32.41
- Type
- journal article
- Language
- en
- Open access
- green
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