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cs.IT2024
Riemannian Gradient Descent Method to Joint Blind Super-Resolution and Demixing in ISAC
Zeyu Xiang, Haifeng Wang, Jiayi Lv +4
Integrated Sensing and Communication (ISAC) has emerged as a promising technology for next-generation wireless networks. In this work, we tackle an ill-posed parameter estimation p…
cs.IT2024
Fast and Provable Simultaneous Blind Super-Resolution and Demixing for Point Source Signals: Scaled Gradient Descent without Regularization
Jinchi Chen
We address the problem of simultaneously recovering a sequence of point source signals from observations limited to the low-frequency end of the spectrum of their summed convolutio…
cs.IT2024
Simultaneous Blind Demixing and Super-resolution via Vectorized Hankel Lift
Haifeng Wang, Jinchi Chen, Hulei Fan +2
In this work, we investigate the problem of simultaneous blind demixing and super-resolution. Leveraging the subspace assumption regarding unknown point spread functions, this prob…