20,164 research outputs found
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
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
Efficient Downlink Channel Probing and Uplink Feedback in FDD Massive MIMO Systems
Massive Multiple-Input Multiple-Output (massive MIMO) is a variant of
multi-user MIMO in which the number of antennas at each Base Station (BS) is
very large and typically much larger than the number of users simultaneously
served. Massive MIMO can be implemented with Time Division Duplexing (TDD) or
Frequency Division Duplexing (FDD) operation. FDD massive MIMO systems are
particularly desirable due to their implementation in current wireless networks
and their efficiency in situations with symmetric traffic and delay-sensitive
applications. However, implementing FDD massive MIMO systems is known to be
challenging since it imposes a large feedback overhead in the Uplink (UL) to
obtain channel state information for the Downlink (DL). In recent years, a
considerable amount of research is dedicated to developing methods to reduce
the feedback overhead in such systems. In this paper, we use the sparse spatial
scattering properties of the environment to achieve this goal. The idea is to
estimate the support of the continuous, frequency-invariant scattering function
from UL channel observations and use this estimate to obtain the support of the
DL channel vector via appropriate interpolation. We use the resulting support
estimate to design an efficient DL probing and UL feedback scheme in which the
feedback dimension scales proportionally with the sparsity order of DL channel
vectors. Since the sparsity order is much less than the number of BS antennas
in almost all practically relevant scenarios, our method incurs much less
feedback overhead compared with the currently proposed methods in the
literature, such as those based on compressed-sensing. We use numerical
simulations to assess the performance of our probing-feedback algorithm and
compare it with these methods.Comment: 24 pages, 10 figure
Channel Estimation for Millimeter Wave Multiuser MIMO Systems via PARAFAC Decomposition
We consider the problem of uplink channel estimation for millimeter wave
(mmWave) systems, where the base station (BS) and mobile stations (MSs) are
equipped with large antenna arrays to provide sufficient beamforming gain for
outdoor wireless communications. Hybrid analog and digital beamforming
structures are employed by both the BS and the MS due to hardware constraints.
We propose a layered pilot transmission scheme and a CANDECOMP/PARAFAC (CP)
decomposition-based method for joint estimation of the channels from multiple
users (i.e. MSs) to the BS. The proposed method exploits the sparse scattering
nature of the mmWave channel and the intrinsic multi-dimensional structure of
the multiway data collected from multiple modes. The uniqueness of the CP
decomposition is studied and sufficient conditions for essential uniqueness are
obtained. The conditions shed light on the design of the beamforming matrix,
the combining matrix and the pilot sequences, and meanwhile provide general
guidelines for choosing system parameters. Our analysis reveals that our
proposed method can achieve a substantial training overhead reduction by
employing the layered pilot transmission scheme. Simulation results show that
the proposed method presents a clear advantage over a compressed sensing-based
method in terms of both estimation accuracy and computational complexity
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
The PAPR Problem in OFDM Transmission: New Directions for a Long-Lasting Problem
Peak power control for multicarrier communications has been a long-lasting
problem in signal processing and communications. However, industry and academia
are confronted with new challenges regarding energy efficient system design.
Particularly, the envisioned boost in network energy efficiency (e.g. at least
by a factor of 1000 in the Green Touch consortium) will tighten the
requirements on component level so that the efficiency gap with respect to
single-carrier transmission must considerably diminish. This paper reflects
these challenges together with a unified framework and new directions in this
field. The combination of large deviation theory, de-randomization and selected
elements of Banach space geometry will offer a novel approach and will provide
ideas and concepts for researchers with a background in industry as well as
those from academia.Comment: Accepted for publication in IEEE Signal Processing Magazin
Millimeter Wave Channel Estimation via Exploiting Joint Sparse and Low-Rank Structures
We consider the problem of channel estimation for millimeter wave (mmWave)
systems, where, to minimize the hardware complexity and power consumption, an
analog transmit beamforming and receive combining structure with only one radio
frequency (RF) chain at the base station (BS) and mobile station (MS) is
employed. Most existing works for mmWave channel estimation exploit sparse
scattering characteristics of the channel. In addition to sparsity, mmWave
channels may exhibit angular spreads over the angle of arrival (AoA), angle of
departure (AoD), and elevation domains. In this paper, we show that angular
spreads give rise to a useful low-rank structure that, along with the sparsity,
can be simultaneously utilized to reduce the sample complexity, i.e. the number
of samples needed to successfully recover the mmWave channel. Specifically, to
effectively leverage the joint sparse and low-rank structure, we develop a
two-stage compressed sensing method for mmWave channel estimation, where the
sparse and low-rank properties are respectively utilized in two consecutive
stages, namely, a matrix completion stage and a sparse recovery stage. Our
theoretical analysis reveals that the proposed two-stage scheme can achieve a
lower sample complexity than a direct compressed sensing method that exploits
only the sparse structure of the mmWave channel. Simulation results are
provided to corroborate our theoretical results and to show the superiority of
the proposed two-stage method
Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues
As a promising paradigm to reduce both capital and operating expenditures,
the cloud radio access network (C-RAN) has been shown to provide high spectral
efficiency and energy efficiency. Motivated by its significant theoretical
performance gains and potential advantages, C-RANs have been advocated by both
the industry and research community. This paper comprehensively surveys the
recent advances of C-RANs, including system architectures, key techniques, and
open issues. The system architectures with different functional splits and the
corresponding characteristics are comprehensively summarized and discussed. The
state-of-the-art key techniques in C-RANs are classified as: the fronthaul
compression, large-scale collaborative processing, and channel estimation in
the physical layer; and the radio resource allocation and optimization in the
upper layer. Additionally, given the extensiveness of the research area, open
issues and challenges are presented to spur future investigations, in which the
involvement of edge cache, big data mining, social-aware device-to-device,
cognitive radio, software defined network, and physical layer security for
C-RANs are discussed, and the progress of testbed development and trial test
are introduced as well.Comment: 27 pages, 11 figure
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
When an attacker meets a cipher-image in 2018: A Year in Review
This paper aims to review the encountered technical contradictions when an
attacker meets the cipher-images encrypted by the image encryption schemes
(algorithms) proposed in 2018 from the viewpoint of an image cryptanalyst. The
most representative works among them are selected and classified according to
their essential structures. Almost all image cryptanalysis works published in
2018 are surveyed due to their small number. The challenging problems on design
and analysis of image encryption schemes are summarized to receive the
attentions of both designers and attackers (cryptanalysts) of image encryption
schemes, which may promote solving scenario-oriented image security problems
with new technologies.Comment: 12 page
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