We are excited to share our latest work on joint time-vertex signal processing published in IEEE Signal Processing Letters! In this paper, we explore the complexities of time-varying graph signals and how they can be more efficiently processed using the joint time-vertex framework. Traditionally, separating signal from noise in these structures has posed significant challenges. However, our research introduces an innovative approach using the fractional Fourier domain, which has proven to achieve lower error rates compared to conventional methods.
🔍 Key Highlights:
- We solve the optimal Wiener filtering problem within a new joint time-vertex fractional Fourier framework.
- Our theoretical analysis and numerical experiments provide a novel perspective and demonstrate superior performance over existing filtering techniques.
The proposed method not only enhances our ability to handle time-varying graph signals but also opens up new possibilities for more sophisticated data analysis in network structures by using parametered transformations.
#jointtimevertex #jtv #wiener #FourierTransform #FractionalFourierTransform #signalprocessing
Paper: https://ieeexplore.ieee.org/document/10518099
Code: https://github.com/koc-lab/jfrt-optimal
Abstract:
Graph signal processing (GSP) uses network structures to analyze and manipulate interconnected signals. Graph signals can also be time-varying. The established joint time-vertex processing framework and corresponding joint time-vertex Fourier transform provide a basis to endeavor such time-varying graph signals. The optimal Wiener filtering problem has been deliberated within the joint time-vertex framework. However, the ordinary Fourier domain is only sometimes optimal for separating the signal and noise; one can achieve lower error rates in a fractional Fourier domain. In this paper, we solve the optimal Wiener filtering problem in the joint time-vertex fractional Fourier domains. We provide a theoretical analysis and numerical experiments with comprehensive comparisons to existing filtering approaches for time-varying graph signals to demonstrate the advantages of our approach.