Performance comparison of reconstruction algorithms in discrete blind multi-coset sampling

Grigoryan, Ruben; Arildsen, Thomas; Tandur, Deepaknath; Larsen, Torben

doi:10.1109/isspit.2012.6621277

Cited by 5 publications

(15 citation statements)

References 13 publications

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“…The Nyquist sampling technique samples discretely the time continuous signal ( ) xt at a fixed time interval T to obtain the sampling sequence   ( ) x m x mT = for 0,1, , 1 mM =− . Discrete multi-coset sampling is a multi-channel discrete periodic non-uniform sub-Nyquist sampling technique [26,27], the structural framework and schematic diagram of the sampling system is shown in Fig. 1.…”

Section: Discrete Multi-coset Samplingmentioning

confidence: 99%

A Joint Optimization Algorithm Using Adaptive Minimum Coset Number Based Discrete Multi-Coset Sampling

Dong

Fan

et al. 2020

IEEE Access

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We address the problem of sparse multi-band signal reconstruction in the case of unknown band position through the discrete multi-coset sampling (DMCS). In this paper, the signal has complex frequency components, and the minimum coset number is determined on the assumption that there is only one frequency component with same characteristics. According to the frequency characteristics, we analyze the influence of the parameterized compressed matrix on the two reconstruction algorithms, and get that a single algorithm does not have universal adaptability to different frequency components. In order to solve this problem, under the discrete multi-coset sampling model, a joint optimization algorithm with discriminant factor (DF-JOA) is proposed to identify the different characteristics and automatically select an appropriate algorithm for signal reconstruction, numerical simulation experiments show the effectiveness of the algorithm. We also simulate the reconstruction success ratio of amplitude and the total coset number under different compressed matrices, determine the influence law, and confirm the improvement of signal reconstruction probability by joint optimization algorithm. Our method ensures the spectrum reconstruction of the multi-band signal. This paper can guide how to better select the coset parameters under the condition that the channels of the discrete multi-coset sampling system are limited but the minimum coset number can be guaranteed. It will have a great significance to the sub-Nyquist sampling technique.INDEX TERMS Discrete multi-coset sampling, sparse reconstruction, minimum coset number, discriminant factor, joint optimization algorithm.

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Section: Discrete Multi-coset Samplingmentioning

confidence: 99%

A Joint Optimization Algorithm Using Adaptive Minimum Coset Number Based Discrete Multi-Coset Sampling

Dong

Fan

et al. 2020

IEEE Access

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“…The windowing procedure in WDBR is also employed in HBFFTR and SBR to avoid the spectral spreading effect. The CTF used in SBR and WDBR employs the OMP reconstruction algorithm for MMV (M-OMP) [21,22] to estimate the joint support S.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Discrete blind reconstruction method for multi‐coset sampling

Dong

Wang

2016

IET signal process.

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Multi-coset sampling is a compressed sampling strategy for the acquisition of spectrally sparse signal. In previous discrete blind recovery methods, either the sensing matrix is really huge, or the sampled sequences are interpolated to the Nyquist rate at the first step, both leading to a high computational complexity. A new discrete blind reconstruction method is proposed in this study to reconstruct a multiband signal from its multi-coset samples. In the proposed method, the fractional delay is implemented at sub-Nyquist rate, so that the sampled sequences do not need to be interpolated to the Nyquist rate until the final step of the reconstruction process. Hence, the computational complexity of the reconstruction method is reduced. Moreover, a windowing procedure is employed in this method to avoid the spectral spreading effect. Numerical experiments are presented to demonstrate that the authors' method outperforms previous methods in terms of computational complexity and accuracy.

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“…Among the ample literature available with regards to sub-Nyquist rate MC sampling and reconstruction of multiband signals, only literature relevant to this paper is mentioned in the sequel. The interested reader is referred to [5]- [17] for more information on related works.…”

Section: B Previous Workmentioning

confidence: 99%

“…The linear system of ( 17) is a restriction of (15) to a finite dimension (or segment size) r since the sample streams of x bb (k) correspond to infinite sequences. Accordingly, solving (17) repeatedly over consecutive segments of size r recovers consecutive segments of baseband sequences X bb . In order to reconstruct sampled sequences of the input signal x(t) at rate 1/T c , it is necessary to frequency shift the baseband sequences to their corresponding spectral slice location and then sum the frequency-shifted sequences.…”

Section: B An MC Segment-based Recovery Frameworkmentioning

confidence: 99%

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Reconstruction of Frequency Hopping Signals From Multi-Coset Samples

Lim,

Wakin

2016

Preprint

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Multi-Coset (MC) sampling is a well established, practically feasible scheme for sampling multiband analog signals below the Nyquist rate. MC sampling has gained renewed interest in the Compressive Sensing (CS) community, due partly to the fact that in the frequency domain, MC sampling bears a strong resemblance to other sub-Nyquist CS acquisition protocols. In this paper, we consider MC sampling of analog frequency hopping signals, which can be viewed as multiband signals with changing band positions. This nonstationarity motivates our consideration of a segment-based reconstruction framework, in which the sample stream is broken into short segments for reconstruction. In contrast, previous works focusing on the reconstruction of multiband signals have used a segmentless reconstruction framework such as the modified MUSIC algorithm. We outline the challenges associated with segmentbased recovery of frequency hopping signals from MC samples, and we explain how these challenges can be addressed using conventional CS recovery techniques. We also demonstrate the utility of the Discrete Prolate Spheroidal Sequences (DPSS's) as an efficient dictionary for reducing the computational complexity of segment-based reconstruction.

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Performance comparison of reconstruction algorithms in discrete blind multi-coset sampling

Cited by 5 publications

References 13 publications

A Joint Optimization Algorithm Using Adaptive Minimum Coset Number Based Discrete Multi-Coset Sampling

A Joint Optimization Algorithm Using Adaptive Minimum Coset Number Based Discrete Multi-Coset Sampling

Discrete blind reconstruction method for multi‐coset sampling

Reconstruction of Frequency Hopping Signals From Multi-Coset Samples

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