Oversampled Adaptive Sensing

Müller R, Bereyhi A, Mecklenbräuker CF (2018)

Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2018

Publisher: IEEE

Event location: San Diego US

DOI: 10.1109/ita.2018.8503191

Open Access Link: https://arxiv.org/abs/1802.03056

Abstract

We develop a Bayesian framework for sensing which adapts the sensing time and/or basis functions to the instantaneous sensing quality measured in terms of the expected posterior mean-squared error. For sparse Gaussian sources a significant reduction in average sensing time and/or mean-squared error is achieved in comparison to non-adaptive sensing. For compression ratio 3, a sparse 10% Gaussian source and equal average sensing times, the proposed method gains about 2 dB over the performance bound of optimum compressive sensing, about 3 dB over non-adaptive 3-fold oversampled orthogonal sensing and about 6 to 7 dB to LASSO-based recovery schemes while enjoying polynomial time complexity.
We utilize that in the presence of Gaussian noise the mean-squared error conditioned on the current observation is proportional to the derivative of the conditional mean estimate with respect to this observation.

Authors with CRIS profile

Ralf Müller Professur für Informationsübertragung Ali Bereyhi Lehrstuhl für Digitale Übertragung (IDC)

Involved external institutions

Technische Universität Wien / Vienna University of Technology AT Austria (AT)

How to cite

APA:

Müller, R., Bereyhi, A., & Mecklenbräuker, C.F. (2018). Oversampled Adaptive Sensing. In Proceedings of the Information Theory and Applications Workshop (ITA). San Diego, US: IEEE.

MLA:

Müller, Ralf, Ali Bereyhi, and Christoph F. Mecklenbräuker. "Oversampled Adaptive Sensing." Proceedings of the Information Theory and Applications Workshop (ITA), San Diego IEEE, 2018.

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