Space-shift sampling of graph signals
Santiago Segarra (University of Pennsylvania)
Antonio G. Marques (King Juan Carlos University)
G Leus (TU Delft - Signal Processing Systems)
Alejandro Ribeiro (University of Pennsylvania)
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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.