1,420 research outputs found
Affine Combination of Two Adaptive Sparse Filters for Estimating Large Scale MIMO Channels
Large scale multiple-input multiple-output (MIMO) system is considered one of
promising technologies for realizing next-generation wireless communication
system (5G) to increasing the degrees of freedom in space and enhancing the
link reliability while considerably reducing the transmit power. However, large
scale MIMO system design also poses a big challenge to traditional
one-dimensional channel estimation techniques due to high complexity and curse
of dimensionality problems which are caused by long delay spread as well as
large number antenna. Since large scale MIMO channels often exhibit sparse
or/and cluster-sparse structure, in this paper, we propose a simple affine
combination of adaptive sparse channel estimation method for reducing
complexity and exploiting channel sparsity in the large scale MIMO system.
First, problem formulation and standard affine combination of adaptive least
mean square (LMS) algorithm are introduced. Then we proposed an effective
affine combination method with two sparse LMS filters and designed an
approximate optimum affine combiner according to stochastic gradient search
method as well. Later, to validate the proposed algorithm for estimating large
scale MIMO channel, computer simulations are provided to confirm effectiveness
of the proposed algorithm which can achieve better estimation performance than
the conventional one as well as traditional method.Comment: 7 pages, 7 figure
IMAC: Impulsive-mitigation adaptive sparse channel estimation based on Gaussian-mixture model
Broadband frequency-selective fading channels usually have the inherent
sparse nature. By exploiting the sparsity, adaptive sparse channel estimation
(ASCE) methods, e.g., reweighted L1-norm least mean square (RL1-LMS), could
bring a performance gain if additive noise satisfying Gaussian assumption. In
real communication environments, however, channel estimation performance is
often deteriorated by unexpected non-Gaussian noises which include conventional
Gaussian noises and impulsive interferences. To design stable communication
systems, hence, it is urgent to develop advanced channel estimation methods to
remove the impulsive interference and to exploit channel sparsity
simultaneously. In this paper, robust impulsive-mitigation adaptive sparse
channel estimation (IMAC) method is proposed for solving aforementioned
technical issues. Specifically, first of all, the non-Gaussian noise model is
described by Gaussian mixture model (GMM). Secondly, cost function of
reweighted L1-norm penalized least absolute error standard (RL1-LAE) algorithm
is constructed. Then, RL1-LAE algorithm is derived for realizing IMAC method.
Finally, representative simulation results are provided to corroborate the
studies.Comment: 12 pages, 10 figures, submitted for journa
Sparse Channel Estimation for MIMO-OFDM Amplify-and-Forward Two-Way Relay Networks
Accurate channel impulse response (CIR) is required for coherent detection
and it can also help improve communication quality of service in
next-generation wireless communication systems. One of the advanced systems is
multi-input multi-output orthogonal frequency-division multiplexing (MIMO-OFDM)
amplify and forward two-way relay networks (AF-TWRN). Linear channel estimation
methods, e.g., least square (LS), have been proposed to estimate the CIR.
However, these methods never take advantage of channel sparsity and then cause
performance loss. In this paper, we propose a sparse channel estimation method
to exploit the sparse structure information in the CIR at each end user. Sparse
channel estimation problem is formulated as compressed sensing (CS) using
sparse decomposition theory and the estimation process is implemented by LASSO
algorithm. Computer simulation results are given to confirm the superiority of
proposed method over the LS-based channel estimation method.Comment: 6 pages, 7 figures, conferenc
Improved adaptive sparse channel estimation using mixed square/fourth error criterion
Sparse channel estimation problem is one of challenge technical issues in
stable broadband wireless communications. Based on square error criterion
(SEC), adaptive sparse channel estimation (ASCE) methods, e.g., zero-attracting
least mean square error (ZA-LMS) algorithm and reweighted ZA-LMS (RZA-LMS)
algorithm, have been proposed to mitigate noise interferences as well as to
exploit the inherent channel sparsity. However, the conventional SEC-ASCE
methods are vulnerable to 1) random scaling of input training signal; and 2)
imbalance between convergence speed and steady state mean square error (MSE)
performance due to fixed step-size of gradient descend method. In this paper, a
mixed square/fourth error criterion (SFEC) based improved ASCE methods are
proposed to avoid aforementioned shortcomings. Specifically, the improved
SFEC-ASCE methods are realized with zero-attracting least mean square/fourth
error (ZA-LMS/F) algorithm and reweighted ZA-LMS/F (RZA-LMS/F) algorithm,
respectively. Firstly, regularization parameters of the SFEC-ASCE methods are
selected by means of Monte-Carlo simulations. Secondly, lower bounds of the
SFEC-ASCE methods are derived and analyzed. Finally, simulation results are
given to show that the proposed SFEC-ASCE methods achieve better estimation
performance than the conventional SEC-ASCE methods. 1Comment: 21 pages, 10 figures, submitted for journa
From Group Sparse Coding to Rank Minimization: A Novel Denoising Model for Low-level Image Restoration
Recently, low-rank matrix recovery theory has been emerging as a significant
progress for various image processing problems. Meanwhile, the group sparse
coding (GSC) theory has led to great successes in image restoration (IR)
problem with each group contains low-rank property. In this paper, we propose a
novel low-rank minimization based denoising model for IR tasks under the
perspective of GSC, an important connection between our denoising model and
rank minimization problem has been put forward. To overcome the bias problem
caused by convex nuclear norm minimization (NNM) for rank approximation, a more
generalized and flexible rank relaxation function is employed, namely weighted
nonconvex relaxation. Accordingly, an efficient iteratively-reweighted
algorithm is proposed to handle the resulting minimization problem combing with
the popular L_(1/2) and L_(2/3) thresholding operators. Finally, our proposed
denoising model is applied to IR problems via an alternating direction method
of multipliers (ADMM) strategy. Typical IR experiments on image compressive
sensing (CS), inpainting, deblurring and impulsive noise removal demonstrate
that our proposed method can achieve significantly higher PSNR/FSIM values than
many relevant state-of-the-art methods.Comment: Accepted by Signal Processin
Least Mean Square/Fourth Algorithm with Application to Sparse Channel Estimation
Broadband signal transmission over frequency-selective fading channel often
requires accurate channel state information at receiver. One of the most
attracting adaptive channel estimation methods is least mean square (LMS)
algorithm. However, LMS-based method is often degraded by random scaling of
input training signal. To improve the estimation performance, in this paper we
apply the standard least mean square/fourth (LMS/F) algorithm to adaptive
channel estimation (ACE). Since the broadband channel is often described by
sparse channel model, such sparsity could be exploited as prior information.
First, we propose an adaptive sparse channel estimation (ASCE) method using
zero-attracting LMS/F (ZA-LMS/F) algorithm. To exploit the sparsity
effectively, an improved channel estimation method is also proposed, using
reweighted zero-attracting LMS/F (RZA-LMS/F) algorithm. We explain the reason
why sparse LMS/F algorithms using l_1-norm sparse constraint function can
improve the estimation performance by virtual of geometrical interpretation. In
addition, for different channel sparsity, we propose a Monte Carlo method to
select a regularization parameter for RA-LMS/F and RZA-LMS/F to achieve
approximate optimal estimation performance. Finally, simulation results show
that the proposed ASCE methods achieve better estimation performance than the
conventional one.Comment: 5pages, 9figure
Block Bayesian Sparse Learning Algorithms With Application to Estimating Channels in OFDM Systems
Cluster-sparse channels often exist in frequencyselective fading broadband
communication systems. The main reason is received scattered waveform exhibits
cluster structure which is caused by a few reflectors near the receiver.
Conventional sparse channel estimation methods have been proposed for general
sparse channel model which without considering the potential cluster-sparse
structure information. In this paper, we investigate the cluster-sparse channel
estimation (CS-CE) problems in the state of the art orthogonal
frequencydivision multiplexing (OFDM) systems. Novel Bayesian clustersparse
channel estimation (BCS-CE) methods are proposed to exploit the cluster-sparse
structure by using block sparse Bayesian learning (BSBL) algorithm. The
proposed methods take advantage of the cluster correlation in training matrix
so that they can improve estimation performance. In addition, different from
our previous method using uniform block partition information, the proposed
methods can work well when the prior block partition information of channels is
unknown. Computer simulations show that the proposed method has a superior
performance when compared with the previous methods.Comment: 5 pages, 6 figures, will be presented in WPMC2014@Sydney, Australi
Adaptive Sparse Channel Estimation for Time-Variant MIMO-OFDM Systems
Accurate channel state information (CSI) is required for coherent detection
in time-variant multiple-input multipleoutput (MIMO) communication systems
using orthogonal frequency division multiplexing (OFDM) modulation. One of
low-complexity and stable adaptive channel estimation (ACE) approaches is the
normalized least mean square (NLMS)-based ACE. However, it cannot exploit the
inherent sparsity of MIMO channel which is characterized by a few dominant
channel taps. In this paper, we propose two adaptive sparse channel estimation
(ASCE) methods to take advantage of such sparse structure information for
time-variant MIMO-OFDM systems. Unlike traditional NLMS-based method, two
proposed methods are implemented by introducing sparse penalties to the cost
function of NLMS algorithm. Computer simulations confirm obvious performance
advantages of the proposed ASCEs over the traditional ACE.Comment: 6 cages,10 figures, conference pape
Adaptive Sparse Channel Estimation for Time-Variant MISO Communication Systems
Channel estimation problem is one of the key technical issues in time-variant
multiple-input single-output (MSIO) communication systems. To estimate the MISO
channel, least mean square (LMS) algorithm is applied to adaptive channel
estimation (ACE). Since the MISO channel is often described by sparse channel
model, such sparsity can be exploited and then estimation performance can be
improved by adaptive sparse channel estimation (ASCE) methods using sparse LMS
algorithms. However, conventional ASCE methods have two main drawbacks: 1)
sensitive to random scale of training signal and 2) unstable in low
signal-to-noise ratio (SNR) regime. To overcome these two harmful factors, in
this paper, we propose a novel ASCE method using normalized LMS (NLMS)
algorithm (ASCE-NLMS). In addition, we also proposed an improved ASCE method
using normalized least mean fourth (NLMF) algorithm (ASCE-NLMF). Two proposed
methods can exploit the channel sparsity effectively. Also, stability of the
proposed methods is confirmed by mathematical derivation. Computer simulation
results show that the proposed sparse channel estimation methods can achieve
better estimation performance than conventional methods.Comment: 5 pages, 7 figures, 1 table, conferenc
Improved Adaptive Sparse Channel Estimation Using Re-Weighted L1-norm Normalized Least Mean Fourth Algorithm
In next-generation wireless communications systems, accurate sparse channel
estimation (SCE) is required for coherent detection. This paper studies SCE in
terms of adaptive filtering theory, which is often termed as adaptive channel
estimation (ACE). Theoretically, estimation accuracy could be improved by
either exploiting sparsity or adopting suitable error criterion. It motivates
us to develop effective adaptive sparse channel estimation (ASCE) methods to
improve estimation performance. In our previous research, two ASCE methods have
been proposed by combining forth-order error criterion based normalized least
mean fourth (NLMF) and L1-norm penalized functions, i.e., zero-attracting NLMF
(ZA-NLMF) algorithm and reweighted ZA-NLMF (RZA-NLMF) algorithm. Motivated by
compressive sensing theory, an improved ASCE method is proposed by using
reweighted L1-norm NLMF (RL1-NLMF) algorithm where RL1 can exploit more
sparsity information than ZA and RZA. Specifically, we construct the cost
function of RL1-NLMF and hereafter derive its update equation. In addition,
intuitive figure is also given to verify that RL1 is more efficient than
conventional two sparsity constraints. Finally, simulation results are provided
to confirm this study.Comment: 6 pages, 11 figures, conference pape
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