An Improved Signal Reconstruction of Modulated Wideband Converter Using a Sensing Matrix Built upon Synchronized Modulated Signals

Wang, Peng; You, Fei; He, Songbai

doi:10.1007/s00034-018-1009-z

Cited by 16 publications

(20 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Signal sparsity indicates the number of non-zero bits in the signal. The results show the signal reconstruction under different signal sparsity and with competing measurement dictionary methods, namely Gaussian random matrix and the gradient matrix [26]. Since the original constructed sparse signal is random, the simulation was repeated 100 times with different signal sparsity, and the average error was calculated.…”

Section: Simulations and Discussionmentioning

confidence: 99%

A cooperative construction method for the measurement matrix and sensing dictionary used in compression sensing

Shen

Cheng

Wang

2020

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

A measurement matrix and sensing dictionary are the basic tools for signal compression sampling and reconstruction, respectively, which are important aspects in the field of compression sensing. Previous studies which have divided the measurement matrix and sensing dictionary into two separate processes did not make full use of their inherent intercorrelations. In case of which could be fully utilized, the mutual coherence of the atoms of measurement matrix and sensing dictionary can be further reduced under the premise of ensuring that the original signal information is stored, which could improve the accuracy of signal recovery. The present study attempted to reduce the mutual coherence between the sensing dictionary and measurement matrix by proposing the t-average mutual coherence coefficient as an evaluation index for the sensing dictionary. A mathematical model for co-constructing a measurement matrix and sensing dictionary is firstly proposed. Then, the measurement matrix and sensing dictionary cooperative construction(MSCA)algorithm is proposed to solve the model at a faster rate. The simulated results for sparse signal and binary image show that the proposed algorithm has faster computing speed and higher solution precision than the state-of-the-art construction algorithms.

show abstract

Section: Simulations and Discussionmentioning

confidence: 99%

A cooperative construction method for the measurement matrix and sensing dictionary used in compression sensing

Shen

Cheng

Wang

2020

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…The sensing matrix of a circuit-level MWC is not well controlled due to the imperfections of the physical devices. As described in [22,23,26], the Fourier coefficients of the PRBSes are not the only factor determining the sensing matrix. Moreover, these Fourier coefficients are also inaccurate due to the waveforms of high-speed PRBSes are distorted during generation and transmission.…”

Section: Background Analysis Of Signal Reconstructionmentioning

confidence: 99%

“…As analyzed in Section 3, the imperfection of physical devices are uncontrollable factors affects the sensing matrix. We propose a method to measure the sensing matrix by some synchronized wideband signals, and the measurement method is displayed in [22] in detail. The measured sensing matrix is denoted as C mea , and it is an accurate estimate of the sensing matrix of the hardware-implemented MWC-based receiver.…”

Section: Simulation and Experimentsmentioning

confidence: 99%

See 1 more Smart Citation

A double screening orthogonal-matching-pursuit algorithm for compressed sensing receiver with high column correlation sensing matrix

Wang

You

et al. 2019

IEICE Electron. Express

Self Cite

View full text Add to dashboard Cite

Modulated wideband converter (MWC) is a multi-branch structure for sub-Nyquist sampling the signals which spectra are sparse. With several band-limited sampling sequences, MWC can recover the signals by compressed sensing (CS) reconstruction algorithms. However, for hardware-implemented MWC, the hardware scale and imperfection of the physical devices lead to strong column correlations of the sensing matrix which may invalidate the classic CS algorithms. Combined with the characteristics of the sensing matrix of the CS receiver, this paper proposes the double screening orthogonal matching pursuit algorithm (DSOMP) to solve this problem. DSOMP updates the support based on the distance from the residual to the column vector subspace of the sensing matrix and adds a secondary screening mechanism to remove strong interference terms. With the strong column correlation sensing matrix of the CS receiver, DSOMP exhibits an excellent support set recovery rate.

show abstract

“…Among them, the modulated wideband converter (MWC), firstly appeared in [6], shows interesting architecture based on the compressed sensing theory to effectively capture sparse multiband signals. Although the recovery process and the reconstruction procedure for the sensed multiband sparse signal is ideally well-defined by sensing matrix of the compressed sensing in mathematical frameworks, the existing implementations including quadrature analog-to-information converter (QAIC), time-segmented QAIC (TS-QAIC) and random triggering based modulated wideband compressive sampling (RT-MWCS) in [7]- [20] face several practical issues related to the non-ideality of the analog components including mixer, lowpass filter (LPF) and analog-to-digital converter (ADC) that cause uncertain deviation on the sensing matrix from its ideal value. Without calibrating the sensing matrix, realization of the reconstruction and the recovery of support becomes infeasible.…”

mentioning

confidence: 99%

A Calibration Technique for Simultaneous Estimation of Actual Sensing Matrix Coefficients on Modulated Wideband Converters

Byambadorj

Asami

Yamaguchi

et al. 2020

IEEE Trans. Circuits Syst. I

View full text Add to dashboard Cite

The Modulated Wideband Converter (MWC) is one of the promising sub-Nyquist sampling architectures for sparse wideband signal sensing applications. Its frequency support detection and the reconstruction ability is well-defined through compressed sensing theory. However, the reconstruction performance of the MWC is strictly limited by the non-ideal components in practical implementations. Previous calibration methods exploit sequential single-tone signals to estimate the actual transfer function of the MWC, which is fairly timeconsuming. We propose a new calibration method that estimates the actual sensing matrix coefficients of the MWC only with a single measurement based on a pilot multi-tone signal. The feasibility of the proposed calibration method is demonstrated in terms of normalized mean square error (NMSE) and image rejection ratio (IRR) of the reconstructed original signal.

show abstract

An Improved Signal Reconstruction of Modulated Wideband Converter Using a Sensing Matrix Built upon Synchronized Modulated Signals

Cited by 16 publications

References 18 publications

A cooperative construction method for the measurement matrix and sensing dictionary used in compression sensing

A cooperative construction method for the measurement matrix and sensing dictionary used in compression sensing

A double screening orthogonal-matching-pursuit algorithm for compressed sensing receiver with high column correlation sensing matrix

A Calibration Technique for Simultaneous Estimation of Actual Sensing Matrix Coefficients on Modulated Wideband Converters

Contact Info

Product

Resources

About