In this paper we analyze spatially multiplexed MIMO systems with limited Channel State Information (CSI) and zero forcing (ZF) linear signal detection technique. Two schemes were considered: Quantization Codebook (QC) and Compressive Sensing (CS). Compressive Sensing is used to generate a reduced CSI feedback to the transmitter in order to reduce feedback load into the system. Performance of th...

This paper gives a survey of sub-Nyquist sampling. Sub-Nyquist sampking is of great interest in the environment that sampling by Nyquist rate is infeasible due to either hardware or software limitation. The survey summarizes a number of recent important researches on sub-Nyquist sampling (compressive sensing). In addition to the basics of sub-Nyquist sampleing, including the fundamental theory ...

The theory of compressive sensing is briefly introduced, and some construction methods for measurement matrix are deduced. A measurement matrix based on Kronecker product is devised, and it has been proved in mathematical proof. Numerical simulations on 2-D image verify that the proposed measurement matrix has better performance in storage space, construction time, and image reconstruction effe...

Intensively growing approach in signal processing and acquisition, the Compressive Sensing approach, allows sparse signals to be recovered from small number of randomly acquired signal coefficients. This paper analyses some of the commonly used threshold-based algorithms for sparse signal reconstruction. Signals satisfy the conditions required by the Compressive Sensing theory. The Orthogonal M...

Compressive sensing is an emerging field in digital signal processing. It introduce a new technique to image reconstruction from less amount of data. This methodology reduces imaging time in MRI. Compressive sensing exploit the sparsity of the signal. In this paper Fractional Fourier is used as sparsifying transform and signal sampled by random sampling . Run length encoding is applied to code ...

Compressive Sensing, as an emerging technique in signal processing is reviewed in this paper together with its’ common applications. As an alternative to the traditional signal sampling, Compressive Sensing allows a new acquisition strategy with significantly reduced number of samples needed for accurate signal reconstruction. The basic ideas and motivation behind this approach are provided in ...

In the compressive phase retrieval problem, or phaseless compressed sensing, or compressed sensing from intensity only measurements, the goal is to reconstruct a sparse or approximately k-sparse vector x ∈ R given access to y = |Φx|, where |v| denotes the vector obtained from taking the absolute value of v ∈ R coordinate-wise. In this paper we present sublinear-time algorithms for different var...

Compressive sensing (CS) is a sampling technique designed for reducing the complexity of sparse data acquisition. One of the major obstacles for practical deployment of CS techniques is the signal reconstruction time and the high storage cost of random sensing matrices. We propose a new structured compressive sensing scheme, based on codes of graphs, that allows for a joint design of structured...

SUMMARY Due to the influence of variations in landform, geophysical data acquisition is usually sub-sampled. Reconstruction of the seismic wavefield from sub-sampled data is an ill-posed inverse problem. Compressive sensing can be used to recover the original geophysical data from the sub-sampled data. In this paper, we consider the wavefield reconstruction problem as a com-pressive sensing and...

The goal of statistical compressive sensing is to efficiently acquire and reconstruct high-dimensional signals with much fewer measurements, given access to a finite set of training signals from the underlying domain being sensed. We present a novel algorithmic framework based on autoencoders that jointly learns the acquisition (a.k.a. encoding) and recovery (a.k.a. decoding) functions while im...