Quantized Spectral Compressed Sensing: Cramer-Rao Bounds and Recovery Algorithms

• Published in: IEEE transactions on signal processing Vol. 66; no. 12; pp. 3268 - 3279
• Main Authors: Fu, Haoyu, Chi, Yuejie
• Format: Journal Article
• Language: English
• Published: IEEE 15.06.2018
• Subjects: Atomic measurements; atomic norms; compressed sensing; Cramér–Rao bound; Frequency measurement; Line spectrum estimation; multiple measurement vectors; Noise measurement; Pollution measurement; quantization; Quantization (signal); Signal processing algorithms; Signal to noise ratio
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2018.2827326

Efficient estimation of wideband spectrum is of great importance for applications such as cognitive radio. Recently, subNyquist sampling schemes based on compressed sensing have been proposed to greatly reduce the sampling rate. However, the important issue of quantization has not been fully addressed, particularly, for high resolution spectrum and parameter estimation. In this paper, we aim to recover spectrally sparse signals and the corresponding parameters, such as frequency and amplitudes, from heavy quantizations of their noisy complex-valued random linear measurements, e.g., only the quadrant information. We first characterize the Cramér-Rao bound under Gaussian noise, which highlights the trade-off between sample complexity and bit depth under different signal-to-noise ratios for a fixed budget of bits. Next, we propose a new algorithm based on atomic norm soft thresholding for signal recovery, which is equivalent to proximal mapping of properly designed surrogate signals with respect to the atomic norm that motivates spectral sparsity. The proposed algorithm can be applied to both the single measurement vector case, as well as the multiple measurement vector case. It is shown that under the Gaussian measurement model, the spectral signals can be reconstructed accurately with high probability, as soon as the number of quantized measurements exceeds the order of <inline-formula><tex-math notation="LaTeX">K\log n</tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">K</tex-math></inline-formula> is the level of spectral sparsity and <inline-formula> <tex-math notation="LaTeX">n</tex-math></inline-formula> is the signal dimension. Finally, numerical simulations are provided to validate the proposed approaches.