Abstract
Multichannel spectral compressed sensing is a fundamental problem in statistical signal processing. In order to reduce the hardware cost and energy consumption, one-bit multichannel spectral compressed sensing is considered. Inspired by rewighted atomic norm minimization, we propose a new method to solve one-bit spectral compressed sensing and prove that each iteration of the proposed method is weighted atomic norm minimization. A new equivalent form of the weighted atomic norm based on Hankel-Toeplitz model is given in this paper. Numerical simulations are given to demonstrate the superior performance of the proposed method.
| Original language | English |
|---|---|
| Pages (from-to) | 8846-8850 |
| Number of pages | 5 |
| Journal | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
| DOIs | |
| State | Published - 2024 |
| Event | 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Seoul, Korea, Republic of Duration: 14 Apr 2024 → 19 Apr 2024 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Hankel-Toeplitz model
- one-bit quantization
- reweighted atomic norm minimization
- spectral compressed sensing
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