Time-Varying Graph Signal Reconstruction

Adaptive Graph Filters in Reproducing Kernel Hilbert Spaces: Design and Performance Analysis

“…In other words, the Fourier transform of k(r l,n − r k,i ) is given by p(v). From (17) and (18), it can be verified that…”

“…compute {z l,n } K l=1 using 17; construct matrix Z n using 21; update h n+1 = h n + µZ n e n ; end where the phase terms (18) where j 2 = −1. In other words, the Fourier transform of k(r l,n − r k,i ) is given by p(v).…”

“…One of the key research areas in GSP is modeling unknown relations between input and output graph signals through a filter [11]- [18]. The application of linear shift-invariant filter models is widely employed in the literature, e.g., to design graph spectral filters [11], [12] and model dynamic graph signals [15], [16].…”

Supervised learning and graph signal processing strategies for beam tracking in highly directional mobile communications

“…19 However, in real datasets, most graph signals are not usually strictly bandlimited and tend to be smooth on graph. 19 However, in real datasets, most graph signals are not usually strictly bandlimited and tend to be smooth on graph.…”

“…19 One way to mitigate this problem is by exploiting the correlations of the signal both on graph and along the time direction that improves significantly the reconstruction quality. However, in many practical problems with real datasets, y is not smooth enough on graph, which can cause poor reconstructions with low quality.…”