Robust detection of random variables using sparse measurements

Authors:
Balakrishnan Narayanaswamy;Rohit Negi;Pradeep Khosla
Affiliations:
Department of Electrical & Computer Engineering, Carnegie Mellon University, Pittsburgh, PA;Department of Electrical & Computer Engineering, Carnegie Mellon University, Pittsburgh, PA;Department of Electrical & Computer Engineering, Carnegie Mellon University, Pittsburgh, PA
Venue:
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Year:
2009

Citing 10
Cited 0

Multiuser Detection

Multiuser Detection
Information Theory and Reliable Communication

Information Theory and Reliable Communication
LT Codes

FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
Performance analysis of linear codes under maximum-likelihood decoding: a tutorial

Communications and Information Theory
DNA Array Decoding from Nonlinear Measurements by Belief Propagation

SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Reliable communication under channel uncertainty

IEEE Transactions on Information Theory
The method of types [information theory]

IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm

IEEE Transactions on Information Theory
The Sensing Capacity of Sensor Networks

IEEE Transactions on Information Theory
Multiuser Detection of Sparsely Spread CDMA

IEEE Journal on Selected Areas in Communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

We look at the problem of estimating k discrete random variables from n noisy and sparse measurements where k = nR, with a 'rate' R. The model is motivated by problems studied in diverse areas including compressed sensing, group testing, multiple access channels and sensor networks. In particular, we study uncertainty and mismatch in the measurement functions and the noise model and quantify the effect of these faults on detection performance, in the large system limit as n → ∞, while R remains constant. We characterize the performance of mismatched and uncertain detectors, design and analyze robust detectors and present an illustrative example where the analysis presented can be used to guide the design of robust measurement ensembles.