590 research outputs found
Multichannel group sparsity methods for compressive channel estimation in doubly selective multicarrier MIMO systems (extended version)
We consider channel estimation within pulse-shaping multicarrier
multiple-input multiple-output (MIMO) systems transmitting over doubly
selective MIMO channels. This setup includes MIMO orthogonal frequency-division
multiplexing (MIMO-OFDM) systems as a special case. We show that the component
channels tend to exhibit an approximate joint group sparsity structure in the
delay-Doppler domain. We then develop a compressive channel estimator that
exploits this structure for improved performance. The proposed channel
estimator uses the methodology of multichannel group sparse compressed sensing,
which combines the methodologies of group sparse compressed sensing and
multichannel compressed sensing. We derive an upper bound on the channel
estimation error and analyze the estimator's computational complexity. The
performance of the estimator is further improved by introducing a basis
expansion yielding enhanced joint group sparsity, along with a basis
optimization algorithm that is able to utilize prior statistical information if
available. Simulations using a geometry-based channel simulator demonstrate the
performance gains due to leveraging the joint group sparsity and optimizing the
basis.Comment: 18 pages, 7 figures, extended version of a paper submitted to IEEE
Trans. Signal Processin
Distributed Compressive Sensing Based Doubly Selective Channel Estimation for Large-Scale MIMO Systems
Doubly selective (DS) channel estimation in largescale multiple-input
multiple-output (MIMO) systems is a challenging problem due to the requirement
of unaffordable pilot overheads and prohibitive complexity. In this paper, we
propose a novel distributed compressive sensing (DCS) based channel estimation
scheme to solve this problem. In the scheme, we introduce the basis expansion
model (BEM) to reduce the required channel coefficients and pilot overheads.
And due to the common sparsity of all the transmit-receive antenna pairs in
delay domain, we estimate the BEM coefficients by considering the DCS
framework, which has a simple linear structure with low complexity. Further
more, a linear smoothing method is proposed to improve the estimation accuracy.
Finally, we conduct various simulations to verify the validity of the proposed
scheme and demonstrate the performance gains of the proposed scheme compared
with conventional schemes.Comment: conference,7 pages,5 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
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
Statistical Recovery of Simultaneously Sparse Time-Varying Signals from Multiple Measurement Vectors
In this paper, we propose a new sparse signal recovery algorithm, referred to
as sparse Kalman tree search (sKTS), that provides a robust reconstruction of
the sparse vector when the sequence of correlated observation vectors are
available. The proposed sKTS algorithm builds on expectation-maximization (EM)
algorithm and consists of two main operations: 1) Kalman smoothing to obtain
the a posteriori statistics of the source signal vectors and 2) greedy tree
search to estimate the support of the signal vectors. Through numerical
experiments, we demonstrate that the proposed sKTS algorithm is effective in
recovering the sparse signals and performs close to the Oracle (genie-based)
Kalman estimator
Structured Compressive Sensing Based Superimposed Pilot Design in Downlink Large-Scale MIMO Systems
Large-scale multiple-input multiple-output (MIMO) with high spectrum and
energy efficiency is a very promising key technology for future 5G wireless
communications. For large-scale MIMO systems, accurate channel state
information (CSI) acquisition is a challenging problem, especially when each
user has to distinguish and estimate numerous channels coming from a large
number of transmit antennas in the downlink. Unlike the conventional orthogonal
pilots whose pilot overhead prohibitively increases with the number of transmit
antennas, we propose a spectrum-efficient superimposed pilot design for
downlink large-scale MIMO scenarios, where frequency-domain pilots of different
transmit antennas occupy the completely same subcarriers in the freqency
domain. Meanwhile, spatial-temporal common sparsity of large-scale MIMO
channels motivates us to exploit the emerging theory of structured compressive
sensing (CS) for reliable MIMO channel estimation, which is realized by the
proposed structured subspace pursuit (SSP) algorithm to simultaneously recover
multiple channels with low pilot overhead. Simulation results demonstrate that
the proposed scheme performs well and can approach the performance bound.Comment: 2 pages, 2 figures.
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6836737&tag=
Broadband Synchronization and Compressive Channel Estimation for Hybrid mmWave MIMO Systems
Synchronization is a fundamental procedure in cellular systems whereby an UE
acquires the time and frequency information required to decode the data
transmitted by a BS. Due to the necessity of using large antenna arrays to
obtain the beamforming gain required to compensate for small antenna aperture,
synchronization must be performed either jointly with beam training as in 5G
NR, or at the low SNR regime if the high-dimensional mmWave MIMO channel is to
be estimated. To circumvent this problem, this work proposes the first
synchronization framework for mmWave MIMO that is robust to both TO, CFO, and
PN synchronization errors and, unlike prior work, implicitly considers the use
of multiple RF chains at both transmitter and receiver. I provide a theoretical
analysis of the estimation problem and derive the HCRLB for the estimation of
both the CFO, PN, and equivalent beamformed channels seen by the different
receive RF chains. I also propose two novel algorithms to estimate the
different unknown parameters, which rely on approximating the MMSE estimator
for the PN and the ML estimators for both the CFO and the equivalent beamformed
channels. Thereafter, I propose to use the estimates for the equivalent
beamformed channels to perform compressive estimation of the high-dimensional
frequency-selective mmWave MIMO channel and thus undergo data transmission. For
performance evaluation, I consider the QuaDRiGa channel simulator, which
implements the 5G NR channel model, and show that both compressive channel
estimation without prior synchronization is possible, and the proposed
approaches outperform current solutions for joint beam training and
synchronization currently considered in 5G NR
Multibeam for Joint Communication and Sensing Using Steerable Analog Antenna Arrays
Beamforming has great potential for joint communication and sensing (JCAS),
which is becoming a demanding feature on many emerging platforms such as
unmanned aerial vehicles and smart cars. Although beamforming has been
extensively studied for communication and radar sensing respectively, its
application in the joint system is not straightforward due to different
beamforming requirements by communication and sensing. In this paper, we
propose a novel multibeam framework using steerable analog antenna arrays,
which allows seamless integration of communication and sensing. Different to
conventional JCAS schemes that support JCAS using a single beam, our framework
is based on the key innovation of multibeam technology: providing fixed subbeam
for communication and packet-varying scanning subbeam for sensing,
simultaneously from a single transmitting array. We provide a system
architecture and protocols for the proposed framework, complying well with
modern packet communication systems with multicarrier modulation. We also
propose low-complexity and effective multibeam design and generation methods,
which offer great flexibility in meeting different communication and sensing
requirements. We further develop sensing parameter estimation algorithms using
conventional digital Fourier transform and 1D compressive sensing techniques,
matching well with the multibeam framework. Simulation results are provided and
validate the effectiveness of our proposed framework, beamforming design
methods and the sensing algorithms.Comment: 14 pages, 10 figures, Journal pape
Frequency-domain Compressive Channel Estimation for Frequency-Selective Hybrid mmWave MIMO Systems
Channel estimation is useful in millimeter wave (mmWave) MIMO communication
systems. Channel state information allows optimized designs of precoders and
combiners under different metrics such as mutual information or
signal-to-interference-noise (SINR) ratio. At mmWave, MIMO precoders and
combiners are usually hybrid, since this architecture provides a means to
trade-off power consumption and achievable rate. Channel estimation is
challenging when using these architectures, however, since there is no direct
access to the outputs of the different antenna elements in the array. The MIMO
channel can only be observed through the analog combining network, which acts
as a compression stage of the received signal. Most of prior work on channel
estimation for hybrid architectures assumes a frequency-flat mmWave channel
model. In this paper, we consider a frequency-selective mmWave channel and
propose compressed-sensing-based strategies to estimate the channel in the
frequency domain. We evaluate different algorithms and compute their complexity
to expose trade-offs in complexity-overhead-performance as compared to those of
previous approaches
Channel Tracking and Hybrid Precoding for Wideband Hybrid Millimeter Wave MIMO Systems
A major source of difficulty when operating with large arrays at mmWave
frequencies is to estimate the wideband channel, since the use of hybrid
architectures acts as a compression stage for the received signal. Moreover,
the channel has to be tracked and the antenna arrays regularly reconfigured to
obtain appropriate beamforming gains when a mobile setting is considered. In
this paper, we focus on the problem of channel tracking for frequency-selective
mmWave channels, and propose two novel channel tracking algorithms that
leverage prior statistical information on the angles-of-arrival and
angles-of-departure. Exploiting this prior information, we also propose a
precoding and combining design method to increase the received SNR during
channel tracking, such that near-optimum data rates can be obtained with
low-overhead. In our numerical results, we analyze the performance of our
proposed algorithms for different system parameters. Simulation results show
that, using channel realizations extracted from the 5G New Radio channel model,
our proposed channel tracking framework is able to achieve near-optimum data
rates
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