Space-shift sampling of graph signals

Conference paper (2016)

Authors

Santiago Segarra University of Pennsylvania
Antonio G. Marques King Juan Carlos University
G. Leus Signal Processing Systems - EEMCS
Alejandro Ribeiro University of Pennsylvania
Research Group
Signal Processing Systems (EEMCS) (TU Delft)
Copyright: © 2016 Santiago Segarra, Antonio G. Marques, G.J.T. Leus, Alejandro Ribeiro
DOI: https://doi.org/10.1109/icassp.2016.7472900
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Published Date

19-05-2016

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

Abstract

A novel scheme for sampling graph signals is proposed. Space-shift sampling can be understood as a hybrid scheme that combines selection sampling -- observing the signal values on a subset of nodes - and aggregation sampling - observing the signal values at a single node after successive aggregation of local data. Under the assumption of bandlimitedness, we state conditions and propose strategies for signal recovery in different settings. Being a more general procedure, space-shift sampling achieves smaller reconstruction errors than current schemes, as we illustrate through the reconstruction of the industrial activity in a graph of the U.S. economy.

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