1,900 research outputs found
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
Sparsity Aware Normalized Least Mean p-power Algorithms with Correntropy Induced Metric Penalty
For identifying the non-Gaussian impulsive noise systems, normalized LMP
(NLMP) has been proposed to combat impulsive-inducing instability. However, the
standard algorithm is without considering the inherent sparse structure
distribution of unknown system. To exploit sparsity as well as to mitigate the
impulsive noise, this paper proposes a sparse NLMP algorithm, i.e., Correntropy
Induced Metric (CIM) constraint based NLMP (CIMNLMP). Based on the first
proposed algorithm, moreover, we propose an improved CIM constraint variable
regularized NLMP(CIMVRNLMP) algorithm by utilizing variable regularized
parameter(VRP) selection method which can further adjust convergence speed and
steady-state error. Numerical simulations are given to confirm the proposed
algorithms.Comment: 5 pages, 4 figures, submitted for DSP201
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
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
Impulsive Noise Mitigation in Powerline Communications Using Sparse Bayesian Learning
Additive asynchronous and cyclostationary impulsive noise limits
communication performance in OFDM powerline communication (PLC) systems.
Conventional OFDM receivers assume additive white Gaussian noise and hence
experience degradation in communication performance in impulsive noise.
Alternate designs assume a parametric statistical model of impulsive noise and
use the model parameters in mitigating impulsive noise. These receivers require
overhead in training and parameter estimation, and degrade due to model and
parameter mismatch, especially in highly dynamic environments. In this paper,
we model impulsive noise as a sparse vector in the time domain without any
other assumptions, and apply sparse Bayesian learning methods for estimation
and mitigation without training. We propose three iterative algorithms with
different complexity vs. performance trade-offs: (1) we utilize the noise
projection onto null and pilot tones to estimate and subtract the noise
impulses; (2) we add the information in the data tones to perform joint noise
estimation and OFDM detection; (3) we embed our algorithm into a decision
feedback structure to further enhance the performance of coded systems. When
compared to conventional OFDM PLC receivers, the proposed receivers achieve SNR
gains of up to 9 dB in coded and 10 dB in uncoded systems in the presence of
impulsive noise.Comment: To appear in IEEE Journal on Selected Areas of Communication
A Unified Approach to Sparse Signal Processing
A unified view of sparse signal processing is presented in tutorial form by
bringing together various fields. For each of these fields, various algorithms
and techniques, which have been developed to leverage sparsity, are described
succinctly. The common benefits of significant reduction in sampling rate and
processing manipulations are revealed.
The key applications of sparse signal processing are sampling, coding,
spectral estimation, array processing, component analysis, and multipath
channel estimation. In terms of reconstruction algorithms, linkages are made
with random sampling, compressed sensing and rate of innovation. The redundancy
introduced by channel coding in finite/real Galois fields is then related to
sampling with similar reconstruction algorithms. The methods of Prony,
Pisarenko, and MUSIC are next discussed for sparse frequency domain
representations. Specifically, the relations of the approach of Prony to an
annihilating filter and Error Locator Polynomials in coding are emphasized; the
Pisarenko and MUSIC methods are further improvements of the Prony method. Such
spectral estimation methods is then related to multi-source location and DOA
estimation in array processing. The notions of sparse array beamforming and
sparse sensor networks are also introduced. Sparsity in unobservable source
signals is also shown to facilitate source separation in SCA; the algorithms
developed in this area are also widely used in compressed sensing. Finally, the
multipath channel estimation problem is shown to have a sparse formulation;
algorithms similar to sampling and coding are used to estimate OFDM channels.Comment: 43 pages, 40 figures, 15 table
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
Study of Robust Distributed Diffusion RLS Algorithms with Side Information for Adaptive Networks
This work develops robust diffusion recursive least squares algorithms to
mitigate the performance degradation often experienced in networks of agents in
the presence of impulsive noise. The first algorithm minimizes an exponentially
weighted least-squares cost function subject to a time-dependent constraint on
the squared norm of the intermediate update at each node. A recursive strategy
for computing the constraint is proposed using side information from the
neighboring nodes to further improve the robustness. We also analyze the
mean-square convergence behavior of the proposed algorithm. The second proposed
algorithm is a modification of the first one based on the dichotomous
coordinate descent iterations. It has a performance similar to that of the
former, however its complexity is significantly lower especially when input
regressors of agents have a shift structure and it is well suited to practical
implementation. Simulations show the superiority of the proposed algorithms
over previously reported techniques in various impulsive noise scenarios.Comment: 15 figures, 17 page
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
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
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