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Electrical Engineering and Systems Science > Signal Processing

arXiv:1912.06762 (eess)
[Submitted on 14 Dec 2019]

Title:Graph Signal Processing: Modulation, Convolution, and Sampling

Authors:John Shi, Jose M. F. Moura
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Abstract:To analyze data supported by arbitrary graphs G, DSP has been extended to Graph Signal Processing (GSP) by redefining traditional DSP concepts like shift, filtering, and Fourier transform among others. This paper revisits modulation, convolution, and sampling of graph signals as appropriate natural extensions of the corresponding DSP concepts. To define these for both the vertex and the graph frequency domains, we associate with generic data graph G and its graph shift A, a graph spectral shift M and a spectral graph Gs. This leads to a spectral GSP theory that parallels in the graph frequency domain the existing GSP theory in the vertex domain. The paper applies this to design and recovery sampling techniques for data supported by arbitrary directed graphs.
Subjects: Signal Processing (eess.SP)
Cite as: arXiv:1912.06762 [eess.SP]
  (or arXiv:1912.06762v1 [eess.SP] for this version)
  https://doi.org/10.48550/arXiv.1912.06762

Submission history

From: John Shi [view email]
[v1] Sat, 14 Dec 2019 01:12:45 UTC (2,918 KB)
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