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Use your powers wisely: resource allocation in parallel channels

Pearlmutter, Barak A. and Jaramillo, Santiago (2006) Use your powers wisely: resource allocation in parallel channels. In: Proceedings of the 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, 6th - 8th September, 2006, Arlington, VA .

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Abstract

This study evaluates various resource allocation strategies for simultaneous estimation of two independent signals from noisy observations. We focus on strategies that make use of the underlying dynamics of each signal, exploiting the difference in estimation uncertainty between them. This evaluation is done empirically, by exploring the parameter space through computer simulations. Two cases are studied: one in which an initial allocation is maintained during estimation of the variables, and one in which allocation can be dynamically changed at each time step according to the uncertainty of the estimate from each channel. The results suggest that there are conditions in which it is advantageous to assign a high signal-to-noise ratio (SNR) to only one of the signals and guess the other one. Furthermore, comparison between the two allocation strategies shows that the dynamic strategy significantly improves estimation performance in low SNR scenarios when the signals have similar dynamics.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Copyright Notice "©2006 IEEE. Reprinted from Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4053614&isnumber=4053605
Keywords: Estimation theory; Resource allocation; Signal processing; noisy observations; Parallel channels; Parameter space; Signal dynamics; Signal estimation; Signal-to-noise ratio.
Subjects: Science & Engineering > Computer Science
Item ID: 1361
Identification Number: 10.1109/MLSP.2006.275515
Depositing User: Dr. Barak Pearlmutter
Date Deposited: 18 May 2009 11:30
Journal or Publication Title: Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing
Publisher: IEEE
Refereed: Yes
URI:

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