Theoretical Bounds on MAP Estimation in Distributed Sensing Networks

Conference contribution
(Original article)


Publication Details

Author(s): Bereyhi A, Haghoghatshoar S, Müller R
Publisher: IEEE
Publication year: 2018
Language: English


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.


FAU Authors / FAU Editors

Bereyhi, Ali
Lehrstuhl für Digitale Übertragung
Müller, Ralf Prof. Dr.-Ing.
Professur für Informationsübertragung


External institutions with authors

Technische Universität Berlin


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.

BibTeX: 

Last updated on 2018-22-09 at 15:23