Sampling Theory of Bandlimited Continuous-Time Graph Signals
arXiv:2010.09952
Abstract
A continuous-time graph signal can be viewed as a time series of graph signals. It generalizes both the classical continuous-time signal and ordinary graph signal. Therefore, such a signal can be considered as a function on two domains: the graph domain and the time domain. In this paper, we consider the sampling theory of bandlimited continuous-time graph signals. To formulate the sampling problem, we need to consider the interaction between the graph and time domains. We describe an explicit procedure to determine a discrete sampling set for perfect signal recovery. Moreover, in analogous to the Nyquist-Shannon sampling theorem, we give an explicit formula for the minimal sample rate.
References in corpus (9)
- The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
- Discrete Signal Processing on Graphs
- Discrete Signal Processing on Graphs: Sampling Theory
- Efficient Sampling Set Selection for Bandlimited Graph Signals Using Graph Spectral Proxies
- Signals on Graphs: Uncertainty Principle and Sampling
- Sampling of graph signals with successive local aggregations
- A Spectral Graph Uncertainty Principle
- Irregularity-Aware Graph Fourier Transforms
- A Hilbert Space Theory of Generalized Graph Signal Processing