Sampling theory of jointly bandlimited time-vertex graph signals.

Time-vertex graph signal (TVGS) models describe time-varying data with irregular structures. The bandlimitedness in the joint time-vertex Fourier spectral domain reflects smoothness in both temporal and graph topology. In this paper, we study the critical sampling of three types of TVGS including continuous-time signals, infinite-length sequences, and finite-length sequences in the time domain for each vertex on the graph. For a jointly bandlimited TVGS, we prove a lower bound on sampling density or sampling ratio, which depends on the measure of the spectral support in the joint time-vertex Fourier spectral domain. We also provide a lower bound on the sampling density or sampling ratio of each vertex on sampling sets for perfect recovery. To demonstrate that critical sampling is achievable, we propose the sampling and reconstruction procedures for the different types of TVGS. Finally, we show how the proposed sampling schemes can be applied to numerical as well as real datasets. • We prove the necessary conditions for the stable reconstruction of JBL TVGS. • We prove lower bounds on sampling densities (or ratios) of the signals on subsets of vertices to be sampled. • We construct a multi-band sampling scheme to prove that critical sampling is achievable for any JBL TVGS. [ABSTRACT FROM AUTHOR]