5,365 research outputs found
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
Application of Compressive Sensing Techniques in Distributed Sensor Networks: A Survey
In this survey paper, our goal is to discuss recent advances of compressive
sensing (CS) based solutions in wireless sensor networks (WSNs) including the
main ongoing/recent research efforts, challenges and research trends in this
area. In WSNs, CS based techniques are well motivated by not only the sparsity
prior observed in different forms but also by the requirement of efficient
in-network processing in terms of transmit power and communication bandwidth
even with nonsparse signals. In order to apply CS in a variety of WSN
applications efficiently, there are several factors to be considered beyond the
standard CS framework. We start the discussion with a brief introduction to the
theory of CS and then describe the motivational factors behind the potential
use of CS in WSN applications. Then, we identify three main areas along which
the standard CS framework is extended so that CS can be efficiently applied to
solve a variety of problems specific to WSNs. In particular, we emphasize on
the significance of extending the CS framework to (i). take communication
constraints into account while designing projection matrices and reconstruction
algorithms for signal reconstruction in centralized as well in decentralized
settings, (ii) solve a variety of inference problems such as detection,
classification and parameter estimation, with compressed data without signal
reconstruction and (iii) take practical communication aspects such as
measurement quantization, physical layer secrecy constraints, and imperfect
channel conditions into account. Finally, open research issues and challenges
are discussed in order to provide perspectives for future research directions
Rank Awareness in Joint Sparse Recovery
In this paper we revisit the sparse multiple measurement vector (MMV) problem
where the aim is to recover a set of jointly sparse multichannel vectors from
incomplete measurements. This problem has received increasing interest as an
extension of the single channel sparse recovery problem which lies at the heart
of the emerging field of compressed sensing. However the sparse approximation
problem has origins which include links to the field of array signal processing
where we find the inspiration for a new family of MMV algorithms based on the
MUSIC algorithm. We highlight the role of the rank of the coefficient matrix X
in determining the difficulty of the recovery problem. We derive the necessary
and sufficient conditions for the uniqueness of the sparse MMV solution, which
indicates that the larger the rank of X the less sparse X needs to be to ensure
uniqueness. We also show that the larger the rank of X the less the
computational effort required to solve the MMV problem through a combinatorial
search. In the second part of the paper we consider practical suboptimal
algorithms for solving the sparse MMV problem. We examine the rank awareness of
popular algorithms such as SOMP and mixed norm minimization techniques and show
them to be rank blind in terms of worst case analysis. We then consider a
family of greedy algorithms that are rank aware. The simplest such algorithm is
a discrete version of MUSIC and is guaranteed to recover the sparse vectors in
the full rank MMV case under mild conditions. We extend this idea to develop a
rank aware pursuit algorithm that naturally reduces to Order Recursive Matching
Pursuit (ORMP) in the single measurement case and also provides guaranteed
recovery in the full rank multi-measurement case. Numerical simulations
demonstrate that the rank aware algorithms are significantly better than
existing algorithms in dealing with multiple measurements.Comment: 23 pages, 2 figure
Compressive Spectrum Sensing for Cognitive Radio Networks
A cognitive radio system has the ability to observe and learn from the
environment, adapt to the environmental conditions, and use the radio spectrum
more efficiently. It allows secondary users (SUs) to use the primary users
(PUs) channels when they are not being utilized. Cognitive radio involves three
main processes: spectrum sensing, deciding, and acting. In the spectrum sensing
process, the channel occupancy is measured with spectrum sensing techniques in
order to detect unused channels. In the deciding process, sensing results are
analyzed and decisions are made based on these results. In the acting process,
actions are made by adjusting the transmission parameters to enhance the
cognitive radio performance.
One of the main challenges of cognitive radio is the wideband spectrum
sensing. Existing spectrum sensing techniques are based on a set of
observations sampled by an ADC at the Nyquist rate. However, those techniques
can sense only one channel at a time because of the hardware limitations on the
sampling rate. In addition, in order to sense a wideband spectrum, the wideband
is divided into narrow bands or multiple frequency bands. SUs have to sense
each band using multiple RF frontends simultaneously, which can result in a
very high processing time, hardware cost, and computational complexity. In
order to overcome this problem, the signal sampling should be as fast as
possible even with high dimensional signals. Compressive sensing has been
proposed as a low-cost solution to reduce the processing time and accelerate
the scanning process. It allows reducing the number of samples required for
high dimensional signal acquisition while keeping the essential information.Comment: PhD dissertation, Advisors: Dr. Naima Kaabouch and Dr. Hassan El
Ghaz
Compressed Sensing for Wireless Communications : Useful Tips and Tricks
As a paradigm to recover the sparse signal from a small set of linear
measurements, compressed sensing (CS) has stimulated a great deal of interest
in recent years. In order to apply the CS techniques to wireless communication
systems, there are a number of things to know and also several issues to be
considered. However, it is not easy to come up with simple and easy answers to
the issues raised while carrying out research on CS. The main purpose of this
paper is to provide essential knowledge and useful tips that wireless
communication researchers need to know when designing CS-based wireless
systems. First, we present an overview of the CS technique, including basic
setup, sparse recovery algorithm, and performance guarantee. Then, we describe
three distinct subproblems of CS, viz., sparse estimation, support
identification, and sparse detection, with various wireless communication
applications. We also address main issues encountered in the design of CS-based
wireless communication systems. These include potentials and limitations of CS
techniques, useful tips that one should be aware of, subtle points that one
should pay attention to, and some prior knowledge to achieve better
performance. Our hope is that this article will be a useful guide for wireless
communication researchers and even non-experts to grasp the gist of CS
techniques
A Survey: Non-Orthogonal Multiple Access with Compressed Sensing Multiuser Detection for mMTC
One objective of the 5G communication system and beyond is to support massive
machine type of communication (mMTC) to propel the fast growth of diverse
Internet of Things use cases. The mMTC aims to provide connectivity to tens of
billions sensor nodes. The dramatic increase of sensor devices and massive
connectivity impose critical challenges for the network to handle the enormous
control signaling overhead with limited radio resource. Non-Orthogonal Multiple
Access (NOMA) is a new paradigm shift in the design of multiple user detection
and multiple access. NOMA with compressive sensing based multiuser detection is
one of the promising candidates to address the challenges of mMTC. The survey
article aims at providing an overview of the current state-of-art research work
in various compressive sensing based techniques that enable NOMA. We present
characteristics of different algorithms and compare their pros and cons,
thereby provide useful insights for researchers to make further contributions
in NOMA using compressive sensing techniques
Applications of Compressed Sensing in Communications Networks
This paper presents a tutorial for CS applications in communications
networks. The Shannon's sampling theorem states that to recover a signal, the
sampling rate must be as least the Nyquist rate. Compressed sensing (CS) is
based on the surprising fact that to recover a signal that is sparse in certain
representations, one can sample at the rate far below the Nyquist rate. Since
its inception in 2006, CS attracted much interest in the research community and
found wide-ranging applications from astronomy, biology, communications, image
and video processing, medicine, to radar. CS also found successful applications
in communications networks. CS was applied in the detection and estimation of
wireless signals, source coding, multi-access channels, data collection in
sensor networks, and network monitoring, etc. In many cases, CS was shown to
bring performance gains on the order of 10X. We believe this is just the
beginning of CS applications in communications networks, and the future will
see even more fruitful applications of CS in our field.Comment: 18 page
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
Compressive Sensing with Prior Support Quality Information and Application to Massive MIMO Channel Estimation with Temporal Correlation
In this paper, we consider the problem of compressive sensing (CS) recovery
with a prior support and the prior support quality information available.
Different from classical works which exploit prior support blindly, we shall
propose novel CS recovery algorithms to exploit the prior support adaptively
based on the quality information. We analyze the distortion bound of the
recovered signal from the proposed algorithm and we show that a better quality
prior support can lead to better CS recovery performance. We also show that the
proposed algorithm would converge in \mathcal{O}\left(\log\mbox{SNR}\right)
steps. To tolerate possible model mismatch, we further propose some robustness
designs to combat incorrect prior support quality information. Finally, we
apply the proposed framework to sparse channel estimation in massive MIMO
systems with temporal correlation to further reduce the required pilot training
overhead.Comment: 14 double-column pages, accepted for publication in IEEE transactions
on signal processing in May, 201
Group Sparse Recovery via the Penalty: Theory and Algorithm
In this work we propose and analyze a novel approach for group sparse
recovery. It is based on regularized least squares with an
penalty, which penalizes the number of nonzero groups. One distinct feature of
the approach is that it has the built-in decorrelation mechanism within each
group, and thus can handle challenging strong inner-group correlation. We
provide a complete analysis of the regularized model, e.g., existence of a
global minimizer, invariance property, support recovery, and properties of
block coordinatewise minimizers. Further, the regularized problem admits an
efficient primal dual active set algorithm with a provable finite-step global
convergence. At each iteration, it involves solving a least-squares problem on
the active set only, and exhibits a fast local convergence, which makes the
method extremely efficient for recovering group sparse signals. Extensive
numerical experiments are presented to illustrate salient features of the model
and the efficiency and accuracy of the algorithm. A comparative study indicates
its competitiveness with existing approaches.Comment: 15 pp, to appear at IEEE Transactions on Signal Processin
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