Design of a Multi-Channel Random Demodulator for Wideband Signals

2014 ◽  
Vol 644-650 ◽  
pp. 4221-4224
Author(s):  
Jian Lin Zhao ◽  
Wei Qing Huang ◽  
Zhi Qiang Lv ◽  
Xue Jie Ding
Keyword(s):  
Compressive Sensing ◽  
Sampling Rate ◽  
Prior Work ◽  
Measurement Matrix ◽  
Switching Scheme ◽  
Wideband Signals ◽  
Reconstructed Signal ◽  
Random Demodulator ◽  
Tone Signal ◽  
Nyquist Rate

The wideband signals in most important applications are sparse or compressible in some sense. A multi-channel scheme for random demodulator without integrator is introduced in this paper. This architecture is based on compressive sensing (CS) and random demodulator (RD), and overcomes the problem of the integrator’s switching scheme injects noise into the signal and deteriorates the reconstructed signal of the RD, which has the same reconstruction guarantees by similar algorithms with the basic RD because the measurement matrix between their is identical, and which resolves some of the practical issues present in prior work. The results of simulation indicate that multi-tone signal can be successful reconstructed at sampling rate downs to 1/10 of the Nyquist-rate, which represents an up to 90% savings in the bandwidth and the storage memory.

scholarly journals Compressed Detection for Pulse-Based Communications in the Terahertz Band

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Pankaj Singh ◽  
Byung-Wook Kim ◽  
Sung-Yoon Jung
Keyword(s):  
Channel Estimation ◽  
Matching Pursuit ◽  
Sampling Rate ◽  
Design Parameters ◽  
Hardware Complexity ◽  
Analog To Digital ◽  
High Data ◽  
Formidable Challenge ◽  
Reconstructed Signal ◽  
Nyquist Rate

Terahertz (THz) band (0.1-10 THz)-based electromagnetic communications are envisioned as a key technology to enable future high-data-rate short-range ultrabroadband communications. However, one of the fundamental bottlenecks is the efficiency of the analog-to-digital converters (ADCs) considering the formidable challenge of sampling the signal at the Nyquist rate eventually increasing transceiver design complexity. The Compressed sensing (CS) framework enables the successful reconstruction of sparse signals from a small set of projections onto a random vector which would lead to sub-Nyquist rate sampling. In this paper, THz band channel estimation based on the theory of CS is developed. The proposed approach exploits the fact that transmitting an ultrashort pulse through a multipath THz channel leads to a received THz signal that can be approximated by a linear combination of a few atoms from a predefined dictionary, yielding thus a sparse representation of the received signal. The fundamental idea is in the design of the dictionary of atoms that closely matches the transmitted pulse leading thus to a higher probability of CS reconstruction. The Orthogonal Matching Pursuit (OMP) algorithm is used to identify the strongest atoms in the projected signal. This reconstructed signal is subsequently used as a reference template in a correlator-based detector. The bit error rate (BER) performance of the proposed detector is analyzed and compared with the conventional CS-based channel estimation and reconstruction approach. Extensive simulations show that, for different design parameters, our proposed detector outperforms the traditional CS-based correlator receiver for the same sampling rate leading thus to a much-reduced use of analog-to-digital resources. Moreover, the proposed detector has been shown to reduce the hardware complexity of the receiver by significantly reducing the number of parallel mixer-integration branches.

Downsampling Direct Sequence Spread Spectrum Signal Using Orthogonal Pretreatment

2014 ◽  
Vol 610 ◽  
pp. 944-948
Author(s):  
Jin Bo Zhang ◽  
Jian Cheng ◽  
Lei Yang
Keyword(s):  
Spread Spectrum ◽  
Strong Correlation ◽  
Sampling Rate ◽  
Direct Sequence Spread Spectrum ◽  
Direct Sequence ◽  
Sensing Matrix ◽  
Pretreatment Method ◽  
Reconstructed Signal ◽  
Nyquist Rate ◽  
Simulation Results

The Direct Sequence Spread Spectrum (DSSS) signal is widely used because of its good concealment and anti-jamming performance. The Compressive Sensing (CS) theory reduced the sampling rate of the DSSS signal effectively compared with the traditional Nyquist-rate sampling theory. While in the process of CS sampling the sensing matrix and the sparse basis generally have a strong correlation when the DSSS signal is decomposed with a complete dictionary. This paper presents a novel orthogonal pretreatment method with which the incoherence between sensing matrix and sparse basis can be improved. As a result, the reconstructed signal is more accurate. Simulation results demonstrate that this method is effective and efficient.

scholarly journals COMPRESSIVE SENSING

2020 ◽  
Vol 5 (2) ◽  
pp. 249-259
Author(s):  
Abhilasha Sharma
Keyword(s):  
Signal Processing ◽  
Compressive Sensing ◽  
Basic Concept ◽  
Sampling Rate ◽  
New Technique ◽  
Maximum Frequency ◽  
Conventional System ◽  
Nyquist Rate

Compressive sensing is a relatively new technique in the signal processing field which allows acquiring signals while taking few samples. It works on two principles: sparsity, which pertains to the signals of interest, and incoherence, which pertains to the sensing modality. Since, in conventional system all signals follow the Nyquist criteria, in which the sampling rate must be at least twice the maximum frequency of modulating signal. But, in this new concept we can recover the signal below the Nyquist rate. This paper presents the basic concept of compressive sensing and area of applications, where we can apply this technique.                                      

Research on Performance of Analog to Information Converter

2013 ◽  
Vol 333-335 ◽  
pp. 601-604
Author(s):  
Bei Bei Tang ◽  
Yun Zhang ◽  
Zhi Jing Xu ◽  
Jun Li
Keyword(s):  
Compressive Sensing ◽  
Impulse Response ◽  
Continuous Time ◽  
Sampling Rate ◽  
Time Signal ◽  
Signal Recovery ◽  
Analog Filter ◽  
Discrete Signals ◽  
Low Pass ◽  
Random Demodulator

Compressive Sensing (CS) Theory enables sampling discrete signals with quite lower sampling rate compared with traditional Nyquist sampling rate and guaranteeing faithful reconstruction. Based on CS theory, Analog-to-Information Conversion (AIC) was proposed to process continuous-time signal. In this paper, the framework of Analog-to-Information Converter is composed by a pseudo-random demodulator, a low pass analog filter and a low speed sampler. And we mainly discuss the damage on the signal recovery produced by lower and higher orders of filter impulse response.

scholarly journals Compressed Measurements Based Spectrum Sensing for Wideband Cognitive Radio Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Taha A. Khalaf ◽  
Mohammed Y. Abdelsadek ◽  
Mohammed Farrag
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Sampling Rate ◽  
Energy Detection ◽  
Operating Characteristics ◽  
Reconstructed Signal ◽  
Detection Probabilities ◽  
Radio Systems ◽  
Nyquist Rate ◽  
Analytical Expressions

Spectrum sensing is the most important component in the cognitive radio (CR) technology. Spectrum sensing has considerable technical challenges, especially in wideband systems where higher sampling rates are required which increases the complexity and the power consumption of the hardware circuits. Compressive sensing (CS) is successfully deployed to solve this problem. Although CS solves the higher sampling rate problem, it does not reduce complexity to a large extent. Spectrum sensing via CS technique is performed in three steps: sensing compressed measurements, reconstructing the Nyquist rate signal, and performing spectrum sensing on the reconstructed signal. Compressed detectors perform spectrum sensing from the compressed measurements skipping the reconstruction step which is the most complex step in CS. In this paper, we propose a novel compressed detector using energy detection technique on compressed measurements sensed by the discrete cosine transform (DCT) matrix. The proposed algorithm not only reduces the computational complexity but also provides a better performance than the traditional energy detector and the traditional compressed detector in terms of the receiver operating characteristics. We also derive closed form expressions for the false alarm and detection probabilities. Numerical results show that the analytical expressions coincide with the exact probabilities obtained from simulations.

An Algorithm of Image Coding Based on Compressive Sensing

2013 ◽  
Vol 427-429 ◽  
pp. 1849-1852
Author(s):  
Dong Cheng Shi ◽  
Yi Dan Xing ◽  
Xiao Ding Shi
Keyword(s):  
Image Reconstruction ◽  
Compressive Sensing ◽  
Image Coding ◽  
Sampling Rate ◽  
Measurement Matrix ◽  
Block Algorithm ◽  
Optimum Parameters ◽  
Block Sizes ◽  
Reconstruction Model ◽  
Reconstruction Time

Block Compressive Sensing (BCS) is a image reconstruction model based on CS theory. By use the same measurement matrix to obtain the data in the form of Block × Block. Algorithm meaning to solve the problem that the traditional CS measurement matrix required for large storage, but different block has important influence on reconstruction time and effect. In this paper, find out the optimum parameters of the block. By compared the PSNR and reconstructed image effect under different sampling rate and different block sizes.

scholarly journals Compressive sensing-based vibration signal reconstruction using sparsity adaptive subspace pursuit

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879087 ◽  
Author(s):  
Lin Zhou ◽  
Qianxiang Yu ◽  
Daozhi Liu ◽  
Ming Li ◽  
Shukai Chi ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Data Storage ◽  
Wireless Sensors ◽  
Signal Reconstruction ◽  
Estimation Method ◽  
Vibration Signal ◽  
Estimation Accuracy ◽  
Offshore Structure ◽  
Vibration Signals ◽  
Reconstructed Signal

Wireless sensors produce large amounts of data in long-term online monitoring following the Shannon–Nyquist theorem, leading to a heavy burden on wireless communications and data storage. To address this problem, compressive sensing which allows wireless sensors to sample at a much lower rate than the Nyquist frequency has been considered. However, the lower rate sacrifices the integrity of the signal. Therefore, reconstruction from low-dimension measurement samples is necessary. Generally, the reconstruction needs the information of signal sparsity in advance, whereas it is usually unknown in practical applications. To address this issue, a sparsity adaptive subspace pursuit compressive sensing algorithm is deployed in this article. In order to balance the computational speed and estimation accuracy, a half-fold sparsity estimation method is proposed. To verify the effectiveness of this algorithm, several simulation tests were performed. First, the feasibility of subspace pursuit algorithm is verified using random sparse signals with five different sparsities. Second, the synthesized vibration signals for four different compression rates are reconstructed. The corresponding reconstruction correlation coefficient and root mean square error are demonstrated. The high correlation and low error result mean that the proposed algorithm can be applied in the vibration signal process. Third, implementation of the proposed approach for a practical vibration signal from an offshore structure is carried out. To reduce the effect of signal noise, the wavelet de-noising technique is used. Considering the randomness of the sampling, many reconstruction tests were carried out. Finally, to validate the reliability of the reconstructed signal, the structure modal parameters are calculated by the Eigensystem realization algorithm, and the result is only slightly different between original and reconstructed signal, which means that the proposed method can successfully save the modal information of vibration signals.

Random Acquisition in Compressive Sensing

2021 ◽  
Vol 12 (3) ◽  
pp. 140-165
Author(s):  
Mahdi Khosravy ◽  
Thales Wulfert Cabral ◽  
Max Mateus Luiz ◽  
Neeraj Gupta ◽  
Ruben Gonzalez Crespo
Keyword(s):  
Compressive Sensing ◽  
Sampling Theorem ◽  
Lower Number ◽  
Compressive Sampling ◽  
Minimum Requirement ◽  
Random Modulation ◽  
Random Demodulator ◽  
Modulated Wideband Converter ◽  
Signal Image ◽  
Sparsity Structure

Compressive sensing has the ability of reconstruction of signal/image from the compressive measurements which are sensed with a much lower number of samples than a minimum requirement by Nyquist sampling theorem. The random acquisition is widely suggested and used for compressive sensing. In the random acquisition, the randomness of the sparsity structure has been deployed for compressive sampling of the signal/image. The article goes through all the literature up to date and collects the main methods, and simply described the way each of them randomly applies the compressive sensing. This article is a comprehensive review of random acquisition techniques in compressive sensing. Theses techniques have reviews under the main categories of (1) random demodulator, (2) random convolution, (3) modulated wideband converter model, (4) compressive multiplexer diagram, (5) random equivalent sampling, (6) random modulation pre-integration, (7) quadrature analog-to-information converter, (8) randomly triggered modulated-wideband compressive sensing (RT-MWCS).

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