1,953 research outputs found
Sparse Channel Estimation with Gradient-Based Algorithms: A comparative Study
Channel state information (CSI) is very crucial for any wireless
communication systems. Typically, CSI can be characterized at the receiver side
using channel impulse response (CIR). Many observations have shown that the CIR
of broadband multipath wireless channels are often sparse. To this point, the
family of least mean square (LMS)-based algorithms have been widely used to
estimate the CIR, unfortunately, the performance of LMS family is not much
accurate in terms of sparse channel estimation. The Least Mean Mixed Norm
(LMMN) algorithm combines the advantages of both the Least Mean square (LMS)
and the Least Mean Fourth (LMF)algorithm, which makes this algorithm stands in
a very special position among the family members in terms of convergence and
steady state error. In this paper, we held a fair comparative study between the
LMMN and a number of the LMS-based algorithms, such as the LMS algorithm, the
zero-attracting (ZA-LMS) algorithm, and the normalized (NLMS) algorithm.
Simulation results are carried out to compare the performance of all these
algorithms with the LMMN algorithm. The results show that the LMMN algorithm
outperforms the rest of these algorithms in the identification of sparse
systems in terms of both fast convergence and the steady state error.Comment: 5 pages, 4 Figures, The 15th International Multi-Conference on
Systems, Signals and Device
Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments
Sparse adaptive channel estimation problem is one of the most important
topics in broadband wireless communications systems due to its simplicity and
robustness. So far many sparsity-aware channel estimation algorithms have been
developed based on the well-known minimum mean square error (MMSE) criterion,
such as the zero-attracting least mean square (ZALMS), which are robust under
Gaussian assumption. In non-Gaussian environments, however, these methods are
often no longer robust especially when systems are disturbed by random
impulsive noises. To address this problem, we propose in this work a robust
sparse adaptive filtering algorithm using correntropy induced metric (CIM)
penalized maximum correntropy criterion (MCC) rather than conventional MMSE
criterion for robust channel estimation. Specifically, MCC is utilized to
mitigate the impulsive noise while CIM is adopted to exploit the channel
sparsity efficiently. Both theoretical analysis and computer simulations are
provided to corroborate the proposed methods.Comment: 29 pages, 12 figures, accepted by Journal of the Franklin Institut
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
Robust Adaptive Sparse Channel Estimation in the Presence of Impulsive Noises
Broadband wireless channels usually have the sparse nature. Based on the
assumption of Gaussian noise model, adaptive filtering algorithms for
reconstruction sparse channels were proposed to take advantage of channel
sparsity. However, impulsive noises are often existed in many advance broadband
communications systems. These conventional algorithms are vulnerable to
deteriorate due to interference of impulsive noise. In this paper, sign least
mean square algorithm (SLMS) based robust sparse adaptive filtering algorithms
are proposed for estimating channels as well as for mitigating impulsive noise.
By using different sparsity-inducing penalty functions, i.e., zero-attracting
(ZA), reweighted ZA (RZA), reweighted L1-norm (RL1) and Lp-norm (LP), the
proposed SLMS algorithms are termed as SLMS-ZA, SLMS-RZA, LSMS-RL1 and SLMS-LP.
Simulation results are given to validate the proposed algorithms.Comment: 5 pages, 4 figures, submitted for DSP2015 conference pape
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
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
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
Iterative-Promoting Variable Step Size Least Mean Square Algorithm for Accelerating Adaptive Channel Estimation
Invariable step size based least-mean-square error (ISS-LMS) was considered
as a very simple adaptive filtering algorithm and hence it has been widely
utilized in many applications, such as adaptive channel estimation. It is well
known that the convergence speed of ISS-LMS is fixed by the initial step-size.
In the channel estimation scenarios, it is very hard to make tradeoff between
convergence speed and estimation performance. In this paper, we propose an
iterative-promoting variable step size based least-mean-square error (VSS-LMS)
algorithm to control the convergence speed as well as to improve the estimation
performance. Simulation results show that the proposed algorithm can achieve
better estimation performance than previous ISS-LMS while without sacrificing
convergence speed.Comment: 6 pages, 8 figures, conferenc
Adaptive MIMO Channel Estimation using Sparse Variable Step-Size NLMS Algorithms
To estimate multiple-input multiple-output (MIMO) channels, invariable
step-size normalized least mean square (ISSNLMS) algorithm was applied to
adaptive channel estimation (ACE). Since the MIMO channel is often described by
sparse channel model due to broadband signal transmission, such sparsity can be
exploited by adaptive sparse channel estimation (ASCE) methods using sparse
ISS-NLMS algorithms. It is well known that step-size is a critical parameter
which controls three aspects: algorithm stability, estimation performance and
computational cost. The previous approaches can exploit channel sparsity but
their step-sizes are keeping invariant which unable balances well the three
aspects and easily cause either estimation performance loss or instability. In
this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS)
algorithms to improve the accuracy of MIMO channel estimators. First, ASCE for
estimating MIMO channels is formulated in MIMO systems. Second, different
sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition,
difference between sparse ISSNLMS algorithms and sparse VSS-NLMS ones are
explained. At last, to verify the effectiveness of the proposed algorithms for
ASCE, several selected simulation results are shown to prove that the proposed
sparse VSS-NLMS algorithms can achieve better estimation performance than the
conventional methods via mean square error (MSE) and bit error rate (BER)
metrics.Comment: 5 papges, 6 figures, submitted for ICCS2014@Macau. arXiv admin note:
substantial text overlap with arXiv:1311.131
ROSA: Robust sparse adaptive channel estimation in the presence of impulsive noises
Based on the assumption of Gaussian noise model, conventional adaptive
filtering algorithms for reconstruction sparse channels were proposed to take
advantage of channel sparsity due to the fact that broadband wireless channels
usually have the sparse nature. However, state-of-the-art algorithms are
vulnerable to deteriorate under the assumption of non-Gaussian noise models
(e.g., impulsive noise) which often exist in many advanced communications
systems. In this paper, we study the problem of RObust Sparse Adaptive channel
estimation (ROSA) in the environment of impulsive noises using variable
step-size affine projection sign algorithm (VSS-APSA). Specifically, standard
VSS-APSA algorithm is briefly reviewed and three sparse VSS-APSA algorithms are
proposed to take advantage of channel sparsity with different sparse
constraints. To fairly evaluate the performance of these proposed algorithms,
alpha-stable noise is considered to approximately model the realistic impulsive
noise environments. Simulation results show that the proposed algorithms can
achieve better performance than standard VSS-APSA algorithm in different
impulsive environments.Comment: 18 pages, 8 figures, submitted for journal. arXiv admin note: text
overlap with arXiv:1502.05484; substantial text overlap with arXiv:1503.0080
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