130 research outputs found
Interference Suppression and Group-Based Power Adjustment via Alternating Optimization for DS-CDMA Networks with Multihop Relaying
This work presents joint interference suppression and power allocation
algorithms for DS-CDMA networks with multiple hops and decode-and-forward (DF)
protocols. A scheme for joint allocation of power levels across the relays
subject to group-based power constraints and the design of linear receivers for
interference suppression is proposed. A constrained minimum mean-squared error
(MMSE) design for the receive filters and the power allocation vectors is
devised along with an MMSE channel estimator. In order to solve the proposed
optimization efficiently, a method to form an effective group of users and an
alternating optimization strategy are devised with recursive alternating least
squares (RALS) algorithms for estimating the parameters of the receiver, the
power allocation and the channels. Simulations show that the proposed
algorithms obtain significant gains in capacity and performance over existing
schemes.Comment: 2 figures. arXiv admin note: substantial text overlap with
arXiv:1301.5912, arXiv:1301.009
Joint Power Adjustment and Interference Mitigation Techniques for Cooperative Spread Spectrum Systems
This paper presents joint power allocation and interference mitigation
techniques for the downlink of spread spectrum systems which employ multiple
relays and the amplify and forward cooperation strategy. We propose a joint
constrained optimization framework that considers the allocation of power
levels across the relays subject to an individual power constraint and the
design of linear receivers for interference suppression. We derive constrained
minimum mean-squared error (MMSE) expressions for the parameter vectors that
determine the optimal power levels across the relays and the linear receivers.
In order to solve the proposed optimization problem efficiently, we develop
joint adaptive power allocation and interference suppression algorithms that
can be implemented in a distributed fashion. The proposed stochastic gradient
(SG) and recursive least squares (RLS) algorithms mitigate the interference by
adjusting the power levels across the relays and estimating the parameters of
the linear receiver. SG and RLS channel estimation algorithms are also derived
to determine the coefficients of the channels across the base station, the
relays and the destination terminal. The results of simulations show that the
proposed techniques obtain significant gains in performance and capacity over
non-cooperative systems and cooperative schemes with equal power allocation.Comment: 6 figures. arXiv admin note: text overlap with arXiv:1301.009
Distributed Low-Rank Adaptive Algorithms Based on Alternating Optimization and Applications
This paper presents a novel distributed low-rank scheme and adaptive
algorithms for distributed estimation over wireless networks. The proposed
distributed scheme is based on a transformation that performs dimensionality
reduction at each agent of the network followed by transmission of a reduced
set of parameters to other agents and reduced-dimension parameter estimation.
Distributed low-rank joint iterative estimation algorithms based on alternating
optimization strategies are developed, which can achieve significantly reduced
communication overhead and improved performance when compared with existing
techniques. A computational complexity analysis of the proposed and existing
low-rank algorithms is presented along with an analysis of the convergence of
the proposed techniques. Simulations illustrate the performance of the proposed
strategies in applications of wireless sensor networks and smart grids.Comment: 12 figures, 13 pages. arXiv admin note: text overlap with
arXiv:1411.112
Low-Rank Signal Processing: Design, Algorithms for Dimensionality Reduction and Applications
We present a tutorial on reduced-rank signal processing, design methods and
algorithms for dimensionality reduction, and cover a number of important
applications. A general framework based on linear algebra and linear estimation
is employed to introduce the reader to the fundamentals of reduced-rank signal
processing and to describe how dimensionality reduction is performed on an
observed discrete-time signal. A unified treatment of dimensionality reduction
algorithms is presented with the aid of least squares optimization techniques,
in which several techniques for designing the transformation matrix that
performs dimensionality reduction are reviewed. Among the dimensionality
reduction techniques are those based on the eigen-decomposition of the observed
data vector covariance matrix, Krylov subspace methods, joint and iterative
optimization (JIO) algorithms and JIO with simplified structures and switching
(JIOS) techniques. A number of applications are then considered using a unified
treatment, which includes wireless communications, sensor and array signal
processing, and speech, audio, image and video processing. This tutorial
concludes with a discussion of future research directions and emerging topics.Comment: 23 pages, 6 figure
Study of Sparsity-Aware Distributed Conjugate Gradient Algorithms for Sensor Networks
This paper proposes distributed adaptive algorithms based on the conjugate
gradient (CG) method and the diffusion strategy for parameter estimation over
sensor networks. We present sparsity-aware conventional and modified
distributed CG algorithms using and log-sum penalty functions. The
proposed sparsity-aware diffusion distributed CG algorithms have an improved
performance in terms of mean square deviation (MSD) and convergence as compared
with the consensus least-mean square (Diffusion-LMS) algorithm, the diffusion
CG algorithms and a close performance to the diffusion distributed recursive
least squares (Consensus-RLS) algorithm. Numerical results show that the
proposed algorithms are reliable and can be applied in several scenarios.Comment: 1 figure, 7 page
Joint Iterative Power Allocation and Linear Interference Suppression Algorithms in Cooperative DS-CDMA Networks
This work presents joint iterative power allocation and interference
suppression algorithms for spread spectrum networks which employ multiple hops
and the amplify-and-forward cooperation strategy for both the uplink and the
downlink. We propose a joint constrained optimization framework that considers
the allocation of power levels across the relays subject to individual and
global power constraints and the design of linear receivers for interference
suppression. We derive constrained linear minimum mean-squared error (MMSE)
expressions for the parameter vectors that determine the optimal power levels
across the relays and the linear receivers. In order to solve the proposed
optimization problems, we develop cost-effective algorithms for adaptive joint
power allocation, and estimation of the parameters of the receiver and the
channels. An analysis of the optimization problem is carried out and shows that
the problem can have its convexity enforced by an appropriate choice of the
power constraint parameter, which allows the algorithms to avoid problems with
local minima. A study of the complexity and the requirements for feedback
channels of the proposed algorithms is also included for completeness.
Simulation results show that the proposed algorithms obtain significant gains
in performance and capacity over existing non-cooperative and cooperative
schemes.Comment: 9 figures; IET Communications, 201
Resource Allocation and Interference Mitigation Techniques for Cooperative Multi-Antenna and Spread Spectrum Wireless Networks
This chapter presents joint interference suppression and power allocation
algorithms for DS-CDMA and MIMO networks with multiple hops and
amplify-and-forward and decode-and-forward (DF) protocols. A scheme for joint
allocation of power levels across the relays and linear interference
suppression is proposed. We also consider another strategy for joint
interference suppression and relay selection that maximizes the diversity
available in the system. Simulations show that the proposed cross-layer
optimization algorithms obtain significant gains in capacity and performance
over existing schemes.Comment: 10 figures. arXiv admin note: substantial text overlap with
arXiv:1301.009
Adaptive Decision Feedback Detection with Parallel Interference Cancellation and Constellation Constraints for Multi-Antenna Systems
In this paper, a novel low-complexity adaptive decision feedback detection
with parallel decision feedback and constellation constraints (P-DFCC) is
proposed for multiuser MIMO systems. We propose a constrained constellation map
which introduces a number of selected points served as the feedback candidates
for interference cancellation. By introducing a reliability checking, a higher
degree of freedom is introduced to refine the unreliable estimates. The P-DFCC
is followed by an adaptive receive filter to estimate the transmitted symbol.
In order to reduce the complexity of computing the filters with time-varying
MIMO channels, an adaptive recursive least squares (RLS) algorithm is employed
in the proposed P-DFCC scheme. An iterative detection and decoding (Turbo)
scheme is considered with the proposed P-DFCC algorithm. Simulations show that
the proposed technique has a complexity comparable to the conventional parallel
decision feedback detector while it obtains a performance close to the maximum
likelihood detector at a low to medium SNR range.Comment: 10 figure
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
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
- …