2,234 research outputs found
Channel Estimation and Hybrid Precoding for Distributed Phased Arrays Based MIMO Wireless Communications
Distributed phased arrays based multiple-input multiple-output (DPA-MIMO) is
a newly introduced architecture that enables both spatial multiplexing and
beamforming while facilitating highly reconfigurable hardware implementation in
millimeter-wave (mmWave) frequency bands. With a DPA-MIMO system, we focus on
channel state information (CSI) acquisition and hybrid precoding. As benefited
from a coordinated and open-loop pilot beam pattern design, all the sub-arrays
can perform channel sounding with less training overhead compared with the
traditional orthogonal operation of each sub-array. Furthermore, two sparse
channel recovery algorithms, known as joint orthogonal matching pursuit (JOMP)
and joint sparse Bayesian learning with reweighting (JSBL-),
are proposed to exploit the hidden structured sparsity in the beam-domain
channel vector. Finally, successive interference cancellation (SIC) based
hybrid precoding through sub-array grouping is illustrated for the DPA-MIMO
system, which decomposes the joint sub-array RF beamformer design into an
interactive per-sub-array-group handle. Simulation results show that the
proposed two channel estimators fully take advantage of the partial coupling
characteristic of DPA-MIMO channels to perform channel recovery, and the
proposed hybrid precoding algorithm is suitable for such array-of-sub-arrays
architecture with satisfactory performance and low complexity.Comment: accepted by IEEE Transactions on Vehicular Technolog
Structured Sparsity: Discrete and Convex approaches
Compressive sensing (CS) exploits sparsity to recover sparse or compressible
signals from dimensionality reducing, non-adaptive sensing mechanisms. Sparsity
is also used to enhance interpretability in machine learning and statistics
applications: While the ambient dimension is vast in modern data analysis
problems, the relevant information therein typically resides in a much lower
dimensional space. However, many solutions proposed nowadays do not leverage
the true underlying structure. Recent results in CS extend the simple sparsity
idea to more sophisticated {\em structured} sparsity models, which describe the
interdependency between the nonzero components of a signal, allowing to
increase the interpretability of the results and lead to better recovery
performance. In order to better understand the impact of structured sparsity,
in this chapter we analyze the connections between the discrete models and
their convex relaxations, highlighting their relative advantages. We start with
the general group sparse model and then elaborate on two important special
cases: the dispersive and the hierarchical models. For each, we present the
models in their discrete nature, discuss how to solve the ensuing discrete
problems and then describe convex relaxations. We also consider more general
structures as defined by set functions and present their convex proxies.
Further, we discuss efficient optimization solutions for structured sparsity
problems and illustrate structured sparsity in action via three applications.Comment: 30 pages, 18 figure
Joint Channel Training and Feedback for FDD Massive MIMO Systems
Massive multiple-input multiple-output (MIMO) is widely recognized as a
promising technology for future 5G wireless communication systems. To achieve
the theoretical performance gains in massive MIMO systems, accurate channel
state information at the transmitter (CSIT) is crucial. Due to the overwhelming
pilot signaling and channel feedback overhead, however, conventional downlink
channel estimation and uplink channel feedback schemes might not be suitable
for frequency-division duplexing (FDD) massive MIMO systems. In addition, these
two topics are usually separately considered in the literature. In this paper,
we propose a joint channel training and feedback scheme for FDD massive MIMO
systems. Specifically, we firstly exploit the temporal correlation of
time-varying channels to propose a differential channel training and feedback
scheme, which simultaneously reduces the overhead for downlink training and
uplink feedback. We next propose a structured compressive sampling matching
pursuit (S-CoSaMP) algorithm to acquire a reliable CSIT by exploiting the
structured sparsity of wireless MIMO channels. Simulation results demonstrate
that the proposed scheme can achieve substantial reduction in the training and
feedback overhead
Data-Driven Learning of a Union of Sparsifying Transforms Model for Blind Compressed Sensing
Compressed sensing is a powerful tool in applications such as magnetic
resonance imaging (MRI). It enables accurate recovery of images from highly
undersampled measurements by exploiting the sparsity of the images or image
patches in a transform domain or dictionary. In this work, we focus on blind
compressed sensing (BCS), where the underlying sparse signal model is a priori
unknown, and propose a framework to simultaneously reconstruct the underlying
image as well as the unknown model from highly undersampled measurements.
Specifically, our model is that the patches of the underlying image(s) are
approximately sparse in a transform domain. We also extend this model to a
union of transforms model that better captures the diversity of features in
natural images. The proposed block coordinate descent type algorithms for blind
compressed sensing are highly efficient, and are guaranteed to converge to at
least the partial global and partial local minimizers of the highly non-convex
BCS problems. Our numerical experiments show that the proposed framework
usually leads to better quality of image reconstructions in MRI compared to
several recent image reconstruction methods. Importantly, the learning of a
union of sparsifying transforms leads to better image reconstructions than a
single adaptive transform.Comment: Appears in IEEE Transactions on Computational Imaging, 201
Transform Learning for Magnetic Resonance Image Reconstruction: From Model-based Learning to Building Neural Networks
Magnetic resonance imaging (MRI) is widely used in clinical practice, but it
has been traditionally limited by its slow data acquisition. Recent advances in
compressed sensing (CS) techniques for MRI reduce acquisition time while
maintaining high image quality. Whereas classical CS assumes the images are
sparse in known analytical dictionaries or transform domains, methods using
learned image models for reconstruction have become popular. The model could be
pre-learned from datasets, or learned simultaneously with the reconstruction,
i.e., blind CS (BCS). Besides the well-known synthesis dictionary model, recent
advances in transform learning (TL) provide an efficient alternative framework
for sparse modeling in MRI. TL-based methods enjoy numerous advantages
including exact sparse coding, transform update, and clustering solutions,
cheap computation, and convergence guarantees, and provide high-quality results
in MRI compared to popular competing methods. This paper provides a review of
some recent works in MRI reconstruction from limited data, with focus on the
recent TL-based methods. A unified framework for incorporating various TL-based
models is presented. We discuss the connections between transform learning and
convolutional or filter bank models and corresponding multi-layer extensions,
with connections to deep learning. Finally, we discuss recent trends in MRI,
open problems, and future directions for the field.Comment: Accepted to IEEE Signal Processing Magazine, Special Issue on
Computational MRI: Compressed Sensing and Beyon
Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration
Low-rank signal modeling has been widely leveraged to capture non-local
correlation in image processing applications. We propose a new method that
employs low-rank tensor factor analysis for tensors generated by grouped image
patches. The low-rank tensors are fed into the alternative direction multiplier
method (ADMM) to further improve image reconstruction. The motivating
application is compressive sensing (CS), and a deep convolutional architecture
is adopted to approximate the expensive matrix inversion in CS applications. An
iterative algorithm based on this low-rank tensor factorization strategy,
called NLR-TFA, is presented in detail. Experimental results on noiseless and
noisy CS measurements demonstrate the superiority of the proposed approach,
especially at low CS sampling rates
Collaborative Sparse Priors for Infrared Image Multi-view ATR
Feature extraction from infrared (IR) images remains a challenging task.
Learning based methods that can work on raw imagery/patches have therefore
assumed significance. We propose a novel multi-task extension of the widely
used sparse-representation-classification (SRC) method in both single and
multi-view set-ups. That is, the test sample could be a single IR image or
images from different views. When expanded in terms of a training dictionary,
the coefficient matrix in a multi-view scenario admits a sparse structure that
is not easily captured by traditional sparsity-inducing measures such as the
-row pseudo norm. To that end, we employ collaborative spike and slab
priors on the coefficient matrix, which can capture fairly general sparse
structures. Our work involves joint parameter and sparse coefficient estimation
(JPCEM) which alleviates the need to handpick prior parameters before
classification. The experimental merits of JPCEM are substantiated through
comparisons with other state-of-art methods on a challenging mid-wave IR image
(MWIR) ATR database made available by the US Army Night Vision and Electronic
Sensors Directorate.Comment: 4 pages, 3 figures, conference pape
Compressive Coded Aperture Keyed Exposure Imaging with Optical Flow Reconstruction
This paper describes a coded aperture and keyed exposure approach to
compressive video measurement which admits a small physical platform, high
photon efficiency, high temporal resolution, and fast reconstruction
algorithms. The proposed projections satisfy the Restricted Isometry Property
(RIP), and hence compressed sensing theory provides theoretical guarantees on
the video reconstruction quality. Moreover, the projections can be easily
implemented using existing optical elements such as spatial light modulators
(SLMs). We extend these coded mask designs to novel dual-scale masks (DSMs)
which enable the recovery of a coarse-resolution estimate of the scene with
negligible computational cost. We develop fast numerical algorithms which
utilize both temporal correlations and optical flow in the video sequence as
well as the innovative structure of the projections. Our numerical experiments
demonstrate the efficacy of the proposed approach on short-wave infrared data.Comment: 13 pages, 4 figures, Submitted to IEEE Transactions on Image
Processing. arXiv admin note: substantial text overlap with arXiv:1111.724
Structured Sparsity via Alternating Direction Methods
We consider a class of sparse learning problems in high dimensional feature
space regularized by a structured sparsity-inducing norm which incorporates
prior knowledge of the group structure of the features. Such problems often
pose a considerable challenge to optimization algorithms due to the
non-smoothness and non-separability of the regularization term. In this paper,
we focus on two commonly adopted sparsity-inducing regularization terms, the
overlapping Group Lasso penalty -norm and the -norm. We
propose a unified framework based on the augmented Lagrangian method, under
which problems with both types of regularization and their variants can be
efficiently solved. As the core building-block of this framework, we develop
new algorithms using an alternating partial-linearization/splitting technique,
and we prove that the accelerated versions of these algorithms require
iterations to obtain an -optimal
solution. To demonstrate the efficiency and relevance of our algorithms, we
test them on a collection of data sets and apply them to two real-world
problems to compare the relative merits of the two norms
A survey of sparse representation: algorithms and applications
Sparse representation has attracted much attention from researchers in fields
of signal processing, image processing, computer vision and pattern
recognition. Sparse representation also has a good reputation in both
theoretical research and practical applications. Many different algorithms have
been proposed for sparse representation. The main purpose of this article is to
provide a comprehensive study and an updated review on sparse representation
and to supply a guidance for researchers. The taxonomy of sparse representation
methods can be studied from various viewpoints. For example, in terms of
different norm minimizations used in sparsity constraints, the methods can be
roughly categorized into five groups: sparse representation with -norm
minimization, sparse representation with -norm (0p1) minimization,
sparse representation with -norm minimization and sparse representation
with -norm minimization. In this paper, a comprehensive overview of
sparse representation is provided. The available sparse representation
algorithms can also be empirically categorized into four groups: greedy
strategy approximation, constrained optimization, proximity algorithm-based
optimization, and homotopy algorithm-based sparse representation. The
rationales of different algorithms in each category are analyzed and a wide
range of sparse representation applications are summarized, which could
sufficiently reveal the potential nature of the sparse representation theory.
Specifically, an experimentally comparative study of these sparse
representation algorithms was presented. The Matlab code used in this paper can
be available at: http://www.yongxu.org/lunwen.html.Comment: Published on IEEE Access, Vol. 3, pp. 490-530, 201
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