569 research outputs found
Robust Adaptive Beamforming Algorithms Based on the Constrained Constant Modulus Criterion
We present a robust adaptive beamforming algorithm based on the worst-case
criterion and the constrained constant modulus approach, which exploits the
constant modulus property of the desired signal. Similarly to the existing
worst-case beamformer with the minimum variance design, the problem can be
reformulated as a second-order cone (SOC) program and solved with interior
point methods. An analysis of the optimization problem is carried out and
conditions are obtained for enforcing its convexity and for adjusting its
parameters. Furthermore, low-complexity robust adaptive beamforming algorithms
based on the modified conjugate gradient (MCG) and an alternating optimization
strategy are proposed. The proposed low-complexity algorithms can compute the
existing worst-case constrained minimum variance (WC-CMV) and the proposed
worst-case constrained constant modulus (WC-CCM) designs with a quadratic cost
in the number of parameters. Simulations show that the proposed WC-CCM
algorithm performs better than existing robust beamforming algorithms.
Moreover, the numerical results also show that the performances of the proposed
low-complexity algorithms are equivalent or better than that of existing robust
algorithms, whereas the complexity is more than an order of magnitude lower.Comment: 11 pages, 8 figures and 4 tables. IET Signal Processing, 201
Blind Adaptive Beamforming Based on Constrained Constant Modulus RLS Algorithm for Smart Antennas
In this paper, we study the performance of blind adaptive beamforming
algorithms for smart antennas in realistic environments. A constrained constant
modulus (CCM) design criterion is described and used for deriving a recursive
least squares (RLS) type optimization algorithm. Furthermore, two kinds of
scenarios are considered in the paper for analyzing its performance.
Simulations are performed to compare the performance of the proposed method to
other well-known methods for blind adaptive beamforming. Results indicate that
the proposed method has a significant faster convergence rate, better
robustness to changeable environments and better tracking capability.Comment: 3 figure
Low-Complexity Constrained Constant Modulus SG-based Beamforming Algorithms with Variable Step Size
In this paper, two low-complexity adaptive step size algorithms are
investigated for blind adaptive beamforming. Both of them are used in a
stochastic gradient (SG) algorithm, which employs the constrained constant
modulus (CCM) criterion as the design approach. A brief analysis is given for
illustrating their properties. Simulations are performed to compare the
performances of the novel algorithms with other well-known methods. Results
indicate that the proposed algorithms achieve superior performance, better
convergence behavior and lower computational complexity in both stationary and
non-stationary environments.Comment: 3 figures, 1 table ICASSP 2008. arXiv admin note: substantial text
overlap with arXiv:1303.184
Reduced-rank Adaptive Constrained Constant Modulus Beamforming Algorithms based on Joint Iterative Optimization of Filters
This paper proposes a reduced-rank scheme for adaptive beamforming based on
the constrained joint iterative optimization of filters. We employ this scheme
to devise two novel reduced-rank adaptive algorithms according to the constant
modulus (CM) criterion with different constraints. The first devised algorithm
is formulated as a constrained joint iterative optimization of a projection
matrix and a reduced-rank filter with respect to the CM criterion subject to a
constraint on the array response. The constrained constant modulus (CCM)
expressions for the projection matrix and the reduced-rank weight vector are
derived, and a low-complexity adaptive algorithm is presented to jointly
estimate them for implementation. The second proposed algorithm is extended
from the first one and implemented according to the CM criterion subject to a
constraint on the array response and an orthogonal constraint on the projection
matrix. The Gram-Schmidt (GS) technique is employed to achieve this orthogonal
constraint and improve the performance. Simulation results are given to show
superior performance of the proposed algorithms in comparison with existing
methods.Comment: 4 figure
Design of Robust Adaptive Beamforming Algorithms Based on Low-Rank and Cross-Correlation Techniques
This work presents cost-effective low-rank techniques for designing robust
adaptive beamforming (RAB) algorithms. The proposed algorithms are based on the
exploitation of the cross-correlation between the array observation data and
the output of the beamformer. Firstly, we construct a general linear equation
considered in large dimensions whose solution yields the steering vector
mismatch. Then, we employ the idea of the full orthogonalization method (FOM),
an orthogonal Krylov subspace based method, to iteratively estimate the
steering vector mismatch in a reduced-dimensional subspace, resulting in the
proposed orthogonal Krylov subspace projection mismatch estimation (OKSPME)
method. We also devise adaptive algorithms based on stochastic gradient (SG)
and conjugate gradient (CG) techniques to update the beamforming weights with
low complexity and avoid any costly matrix inversion. The main advantages of
the proposed low-rank and mismatch estimation techniques are their
cost-effectiveness when dealing with high dimension subspaces or large sensor
arrays. Simulations results show excellent performance in terms of the output
signal-to-interference-plus-noise ratio (SINR) of the beamformer among all the
compared RAB methods.Comment: 11 figures, 12 page
Adaptive Reduced-Rank Constrained Constant Modulus Beamforming Algorithms Based on Joint Iterative Optimization of Filters
This paper proposes a robust reduced-rank scheme for adaptive beamforming
based on joint iterative optimization (JIO) of adaptive filters. The novel
scheme is designed according to the constant modulus (CM) criterion subject to
different constraints, and consists of a bank of full-rank adaptive filters
that forms the transformation matrix, and an adaptive reduced-rank filter that
operates at the output of the bank of filters to estimate the desired signal.
We describe the proposed scheme for both the direct-form processor (DFP) and
the generalized sidelobe canceller (GSC) structures. For each structure, we
derive stochastic gradient (SG) and recursive least squares (RLS) algorithms
for its adaptive implementation. The Gram-Schmidt (GS) technique is applied to
the adaptive algorithms for reformulating the transformation matrix and
improving performance. An automatic rank selection technique is developed and
employed to determine the most adequate rank for the derived algorithms. The
complexity and convexity analyses are carried out. Simulation results show that
the proposed algorithms outperform the existing full-rank and reduced-rank
methods in convergence and tracking performance.Comment: 10 figures; IEEE Transactions on Signal Processing, 201
Study of Efficient Robust Adaptive Beamforming Algorithms Based on Shrinkage Techniques
This paper proposes low-complexity robust adaptive beamforming (RAB)
techniques based on shrinkage methods. We firstly briefly review a
Low-Complexity Shrinkage-Based Mismatch Estimation (LOCSME) batch algorithm to
estimate the desired signal steering vector mismatch, in which the
interference-plus-noise covariance (INC) matrix is also estimated with a
recursive matrix shrinkage method. Then we develop low complexity adaptive
robust version of the conjugate gradient (CG) algorithm to both estimate the
steering vector mismatch and update the beamforming weights. A computational
complexity study of the proposed and existing algorithms is carried out.
Simulations are conducted in local scattering scenarios and comparisons to
existing RAB techniques are provided.Comment: 9 pages, 2 figures. arXiv admin note: text overlap with
arXiv:1505.0678
Adaptive Low-rank Constrained Constant Modulus Beamforming Algorithms using Joint Iterative Optimization of Parameters
This paper proposes a robust reduced-rank scheme for adaptive beamforming
based on joint iterative optimization (JIO) of adaptive filters. The scheme
provides an efficient way to deal with filters with large number of elements.
It consists of a bank of full-rank adaptive filters that forms a transformation
matrix and an adaptive reduced-rank filter that operates at the output of the
bank of filters. The transformation matrix projects the received vector onto a
low-dimension vector, which is processed by the reduced-rank filter to estimate
the desired signal. The expressions of the transformation matrix and the
reduced-rank weight vector are derived according to the constrained constant
modulus (CCM) criterion. Two novel low-complexity adaptive algorithms are
devised for the implementation of the proposed scheme with respect to different
constrained conditions. Simulations are performed to show superior performance
of the proposed algorithms in comparison with the existing methods.Comment: 4 figures, 4 pages. arXiv admin note: substantial text overlap with
arXiv:1303.157
Study of Joint MSINR and Relay Selection Algorithms for Distributed Beamforming
This paper presents joint maximum signal-to-interference-plus-noise ratio
(MSINR) and relay selection algorithms for distributed beamforming. We propose
a joint MSINR and restricted greedy search relay selection (RGSRS) algorithm
with a total relay transmit power constraint that iteratively optimizes both
the beamforming weights at the relays nodes, maximizing the SINR at the
destination. Specifically, we devise a relay selection scheme that based on
greedy search and compare it to other schemes like restricted random relay
selection (RRRS) and restricted exhaustive search relay selection (RESRS). A
complexity analysis is provided and simulation results show that the proposed
joint MSINR and RGSRS algorithm achieves excellent bit error rate (BER) and
SINR performances.Comment: 7 pages, 2 figures. arXiv admin note: text overlap with
arXiv:1707.0095
Robust Low-Rank LCMV Beamforming Algorithms Based on Joint Iterative Optimization Strategies
This chapter presents reduced-rank linearly constrained minimum variance
(LCMV) algorithms based on the concept of joint iterative optimization of
parameters. The proposed reduced-rank scheme is based on a constrained robust
joint iterative optimization (RJIO) of parameters according to the minimum
variance criterion. The robust optimization procedure adjusts the parameters of
a rank-reduction matrix, a reduced-rank beamformer and the diagonal loading in
an alternating manner. LCMV expressions are developed for the design of the
rank-reduction matrix and the reduced-rank beamformer. Stochastic gradient and
recursive least-squares adaptive algorithms are then devised for an efficient
implementation of the RJIO robust beamforming technique. Simulations for a
application in the presence of uncertainties show that the RJIO scheme and
algorithms outperform in convergence and tracking performances existing
algorithms while requiring a comparable complexity.Comment: 7 figures. arXiv admin note: substantial text overlap with
arXiv:1205.439
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