F
Feiyu Wang
Researcher at University of Electronic Science and Technology of China
Publications - 8
Citations - 251
Feiyu Wang is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Wideband & Direction of arrival. The author has an hindex of 4, co-authored 8 publications receiving 165 citations. Previous affiliations of Feiyu Wang include Delft University of Technology.
Papers
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Super-Resolution Compressed Sensing for Line Spectral Estimation: An Iterative Reweighted Approach
TL;DR: This paper considers the line spectral estimation problem and proposes an iterative reweighted method which jointly estimates the sparse signals and the unknown parameters associated with the true dictionary, and achieves super resolution and outperforms other state-of-the-art methods in many cases of practical interest.
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One-Bit Quantization Design and Channel Estimation for Massive MIMO Systems
TL;DR: Simulation results show that the proposed adaptive and random quantization schemes presents a significant performance improvement over the conventional fixed quantization scheme that uses a fixed (typically zero) threshold.
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Phased-Array-Based Sub-Nyquist Sampling for Joint Wideband Spectrum Sensing and Direction-of-Arrival Estimation
TL;DR: In this article, a sub-Nyquist sampling framework was proposed to estimate the carrier frequencies and the DoAs associated with the narrow-band sources, as well as reconstruct the power spectra of these narrowband signals.
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Direction of Arrival Estimation of Wideband Sources Using Sparse Linear Arrays
TL;DR: In this article, two super-resolution off-the-grid DoA estimation approaches based on atomic norm minimization (ANM) were proposed to exploit the joint sparsity from all frequency bins.
Posted Content
Quantization Design and Channel Estimation for Massive MIMO Systems with One-Bit ADCs
TL;DR: Simulation results show that the proposed adaptive quantization (AQ) and RQ schemes, owing to their wisely devised thresholds, present a significant performance improvement over the conventional fixed quantization scheme that uses a fixed threshold, and meanwhile achieve a substantial training overhead reduction for channel estimation.