Theoretical Bounds on MAP Estimation in Distributed Sensing Networks
Bereyhi A, Haghoghatshoar S, Müller R (2018)
Publication Language: English
Publication Type: Conference contribution, Original article
Publication year: 2018
Publisher: IEEE
Event location: IEEE International Symposium on Information Theory (ISIT)
DOI: 10.1109/isit.2018.8437680
Open Access Link: https://arxiv.org/abs/1805.11893v1
Abstract
The typical approach for recovery of spatially correlated signals is regularized least squares with a coupled regularization term. In the Bayesian framework, this algorithm is seen as a maximum-a-posterior estimator whose postulated prior is proportional to the regularization term. In this paper, we study distributed sensing networks in which a set of spatially correlated signals are measured individually at separate terminals, but recovered jointly via a generic maximum-a-posterior estimator. Using the replica method, it is shown that the setting exhibits the decoupling property. For the case with jointly sparse signals, we invoke Bayesian inference and propose the "multi-dimensional soft thresholding" algorithm which is posed as a linear programming. Our investigations depict that the proposed algorithm outperforms the conventional ℓ2,1-norm regularized least squares scheme while enjoying a feasible computational complexity.
Authors with CRIS profile
Ali Bereyhi
Lehrstuhl für Digitale Übertragung (IDC)
Ralf Müller
Professur für Informationsübertragung
Involved external institutions
Germany (DE)How to cite
APA:
Bereyhi, A., Haghoghatshoar, S., & Müller, R. (2018). Theoretical Bounds on MAP Estimation in Distributed Sensing Networks. IEEE International Symposium on Information Theory (ISIT): IEEE.
MLA:
Bereyhi, Ali, Saeid Haghoghatshoar, and Ralf Müller. "Theoretical Bounds on MAP Estimation in Distributed Sensing Networks." IEEE International Symposium on Information Theory (ISIT) IEEE, 2018.
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