arXiv subjects

R. C. de Lamare

Publications and source records attributed to R. C. de Lamare.

At least 163 records · Page 9Linked to original sources

arXiv2014

Data-Adaptive Reduced-Dimension Robust Beamforming Algorithms

We present low complexity, quickly converging robust adaptive beamformers that combine robust Capon beamformer (RCB) methods and data-adaptive Krylov subspace dimensionality reduction techniques. We extend a recently proposed reduced-dimension RCB framework, which ensures proper combination of RCBs with any form of dimensionality reduction that can be expressed using a full-rank dimension reducing transform, providing new results for data-adaptive dimensionality reduction. We consider Krylov subspace methods computed with the Powers-of-R (PoR) and Conjugate Gradient (CG) techniques, illustrating how a fast CG-based algorithm can be formed by beneficially exploiting that the CG-algorithm diagonalizes the reduced-dimension covariance. Our simulations show the benefits of the proposed approaches.

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arXiv2014

Direction Finding Algorithms with Joint Iterative Subspace Optimization

In this paper, a reduced-rank scheme with joint iterative optimization is presented for direction of arrival estimation. A rank-reduction matrix and an auxiliary reduced-rank parameter vector are jointly optimized to calculate the output power with respect to each scanning angle. Subspace algorithms to estimate the rank-reduction matrix and the auxiliary vector are proposed. Simulations are performed to show that the proposed algorithms achieve an enhanced performance over existing algorithms in the studied scenarios.

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arXiv2014

Low-Complexity Robust Data-Adaptive Dimensionality Reduction Based on Joint Iterative Optimization of Parameters

This paper presents a low-complexity robust data-dependent dimensionality reduction based on a modified joint iterative optimization (MJIO) algorithm for reduced-rank beamforming and steering vector estimation. The proposed robust optimization procedure jointly adjusts the parameters of a rank-reduction matrix and an adaptive beamformer. The optimized rank-reduction matrix projects the received signal vector onto a subspace with lower dimension. The beamformer/steering vector optimization is then performed in a reduced-dimension subspace. We devise efficient stochastic gradient and recursive least-squares algorithms for implementing the proposed robust MJIO design. The proposed robust MJIO beamforming algorithms result in a faster convergence speed and an improved performance. Simulation results show that the proposed MJIO algorithms outperform some existing full-rank and reduced-rank algorithms with a comparable complexity.

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arXiv2014

Adaptive Power Allocation Strategies using DSTC in Cooperative MIMO Networks

Adaptive Power Allocation (PA) algorithms with different criteria for a cooperative Multiple-Input Multiple-Output (MIMO) network equipped with Distributed Space-Time Coding (DSTC) are proposed and evaluated. Joint constrained optimization algorithms to determine the power allocation parameters, the channel parameters and the receive filter are proposed for each transmitted stream in each link. Linear receive filter and maximum-likelihood (ML) detection are considered with Amplify-and-Forward (AF) and Decode-and-Forward (DF) cooperation strategies. In the proposed algorithms, the elements in the PA matrices are optimized at the destination node and then transmitted back to the relay nodes via a feedback channel. The effects of the feedback errors are considered. Linear MMSE expressions and the PA matrices depend on each other and are updated iteratively. Stochastic gradient (SG) algorithms are developed with reduced computational complexity. Simulation results show that the proposed algorithms obtain significant performance gains as compared to existing power allocation schemes.

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arXiv2014

Multi-Branch Tomlinson-Harashima Precoding for MU-MIMO Systems: Theory and Algorithms

Tomlinson-Harashima precoding (THP) is a nonlinear processing technique employed at the transmit side and is a dual to the successive interference cancelation (SIC) detection at the receive side. Like SIC detection, the performance of THP strongly depends on the ordering of the precoded symbols. The optimal ordering algorithm, however, is impractical for multiuser MIMO (MU-MIMO) systems with multiple receive antennas due to the fact that the users are geographically distributed. In this paper, we propose a multi-branch THP (MB-THP) scheme and algorithms that employ multiple transmit processing and ordering strategies along with a selection scheme to mitigate interference in MU-MIMO systems. Two types of multi-branch THP (MB-THP) structures are proposed. The first one employs a decentralized strategy with diagonal weighted filters at the receivers of the users and the second uses a diagonal weighted filter at the transmitter. The MB-MMSE-THP algorithms are also derived based on an extended system model with the aid of an LQ decomposition, which is much simpler compared to the conventional MMSE-THP algorithms. Simulation results show that a better bit error rate (BER) performance can be achieved by the proposed MB-MMSE-THP precoder with a small computational complexity increase.

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arXiv2014

Dynamic Topology Adaptation and Distributed Estimation for Smart Grids

This paper presents new dynamic topology adaptation strategies for distributed estimation in smart grids systems. We propose a dynamic exhaustive search--based topology adaptation algorithm and a dynamic sparsity--inspired topology adaptation algorithm, which can exploit the topology of smart grids with poor--quality links and obtain performance gains. We incorporate an optimized combining rule, named Hastings rule into our proposed dynamic topology adaptation algorithms. Compared with the existing works in the literature on distributed estimation, the proposed algorithms have a better convergence rate and significantly improve the system performance. The performance of the proposed algorithms is compared with that of existing algorithms in the IEEE 14--bus system.

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arXiv2013

Massive MIMO Systems: Signal Processing Challenges and Research Trends

This article presents a tutorial on multiuser multiple-antenna wireless systems with a very large number of antennas, known as massive multi-input multi-output (MIMO) systems. Signal processing challenges and future trends in the area of massive MIMO systems are presented and key application scenarios are detailed. A linear algebra approach is considered for the description of the system and data models of massive MIMO architectures. The operational requirements of massive MIMO systems are discussed along with their operation in time-division duplexing mode, resource allocation and calibration requirements. In particular, transmit and receiver processing algorithms are examined in light of the specific needs of massive MIMO systems. Simulation results illustrate the performance of transmit and receive processing algorithms under scenarios of interest. Key problems are discussed and future trends in the area of massive MIMO systems are pointed out.

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arXiv2013

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.

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arXiv2013

Low-Complexity Design of Generalized Block Diagonalization Precoding Algorithms for Multiuser MIMO Systems

Block diagonalization (BD) based precoding techniques are well-known linear transmit strategies for multiuser MIMO (MU-MIMO) systems. By employing BD-type precoding algorithms at the transmit side, the MU-MIMO broadcast channel is decomposed into multiple independent parallel single user MIMO (SU-MIMO) channels and achieves the maximum diversity order at high data rates. The main computational complexity of BD-type precoding algorithms comes from two singular value decomposition (SVD) operations, which depend on the number of users and the dimensions of each user's channel matrix. In this work, low-complexity precoding algorithms are proposed to reduce the computational complexity and improve the performance of BD-type precoding algorithms. We devise a strategy based on a common channel inversion technique, QR decompositions, and lattice reductions to decouple the MU-MIMO channel into equivalent SU-MIMO channels. Analytical and simulation results show that the proposed precoding algorithms can achieve a comparable sum-rate performance as BD-type precoding algorithms, substantial bit error rate (BER) performance gains, and a simplified receiver structure, while requiring a much lower complexity.

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arXiv2013

Adaptive and Iterative Multi-Branch MMSE Decision Feedback Detection Algorithms for MIMO Systems

In this work, decision feedback (DF) detection algorithms based on multiple processing branches for multi-input multi-output (MIMO) spatial multiplexing systems are proposed. The proposed detector employs multiple cancellation branches with receive filters that are obtained from a common matrix inverse and achieves a performance close to the maximum likelihood detector (MLD). Constrained minimum mean-squared error (MMSE) receive filters designed with constraints on the shape and magnitude of the feedback filters for the multi-branch MMSE DF (MB-MMSE-DF) receivers are presented. An adaptive implementation of the proposed MB-MMSE-DF detector is developed along with a recursive least squares-type algorithm for estimating the parameters of the receive filters when the channel is time-varying. A soft-output version of the MB-MMSE-DF detector is also proposed as a component of an iterative detection and decoding receiver structure. A computational complexity analysis shows that the MB-MMSE-DF detector does not require a significant additional complexity over the conventional MMSE-DF detector, whereas a diversity analysis discusses the diversity order achieved by the MB-MMSE-DF detector. Simulation results show that the MB-MMSE-DF detector achieves a performance superior to existing suboptimal detectors and close to the MLD, while requiring significantly lower complexity.

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arXiv2013

Robust Reduced-Rank Adaptive Processing Based on Parallel Subgradient Projection and Krylov Subspace Techniques

In this paper, we propose a novel reduced-rank adaptive filtering algorithm by blending the idea of the Krylov subspace methods with the set-theoretic adaptive filtering framework. Unlike the existing Krylov-subspace-based reduced-rank methods, the proposed algorithm tracks the optimal point in the sense of minimizing the \sinq{true} mean square error (MSE) in the Krylov subspace, even when the estimated statistics become erroneous (e.g., due to sudden changes of environments). Therefore, compared with those existing methods, the proposed algorithm is more suited to adaptive filtering applications. The algorithm is analyzed based on a modified version of the adaptive projected subgradient method (APSM). Numerical examples demonstrate that the proposed algorithm enjoys better tracking performance than the existing methods for the interference suppression problem in code-division multiple-access (CDMA) systems as well as for simple system identification problems.

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arXiv2013

Joint Transmit Diversity Optimization and Relay Selection for Cooperative MIMO Systems using Discrete Stochastic Algorithms

We propose a joint discrete stochastic optimization based transmit diversity selection (TDS) and relay selection (RS) algorithm for decode-and-forward (DF), cooperative MIMO systems with a non-negligible direct path. TDS and RS are performed jointly with continuous least squares channel estimation (CE), linear minimum mean square error (MMSE) receivers are used at all nodes and no inter-relay communication is required. The performance of the proposed scheme is evaluated via bit-error rate (BER) comparisons and diversity analysis, and is shown to converge to the optimum exhaustive solution.

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arXiv2013

Multi-Branch MMSE Decision Feedback Detection Algorithms with Error Propagation Mitigation for Multi-Antenna Systems

In this work we propose novel decision feedback (DF) detection algorithms with error propagation mitigation capabilities for multi-input multi-output (MIMO) spatial multiplexing systems based on multiple processing branches. The novel strategies for detection exploit different patterns, orderings and constraints for the design of the feedforward and feedback filters. We present constrained minimum mean-squared error (MMSE) filters designed with constraints on the shape and magnitude of the feedback filters for the multi-branch MIMO receivers and show that the proposed MMSE design does not require a significant additional complexity over the single-branch MMSE design. The proposed multi-branch MMSE DF detectors are compared with several existing detectors and are shown to achieve a performance close to the optimal maximum likelihood detector while requiring significantly lower complexity.

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arXiv2013

Sparsity-Aware STAP Algorithms Using $L_1$-norm Regularization For Radar Systems

This article proposes novel sparsity-aware space-time adaptive processing (SA-STAP) algorithms with $l_1$-norm regularization for airborne phased-array radar applications. The proposed SA-STAP algorithms suppose that a number of samples of the full-rank STAP data cube are not meaningful for processing and the optimal full-rank STAP filter weight vector is sparse, or nearly sparse. The core idea of the proposed method is imposing a sparse regularization ($l_1$-norm type) to the minimum variance (MV) STAP cost function. Under some reasonable assumptions, we firstly propose a $l_1$-based sample matrix inversion (SMI) to compute the optimal filter weight vector. However, it is impractical due to its matrix inversion, which requires a high computational cost when in a large phased-array antenna. Then, we devise lower complexity algorithms based on conjugate gradient (CG) techniques. A computational complexity comparison with the existing algorithms and an analysis of the proposed algorithms are conducted. Simulation results with both simulated and the Mountain Top data demonstrate that fast signal-to-interference-plus-noise-ratio (SINR) convergence and good performance of the proposed algorithms are achieved.

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arXiv2013

Joint Iterative Power Adjustment and Interference Suppression Algorithms for Cooperative DS-CDMA Networks

This work presents joint iterative power allocation and interference suppression algorithms for DS-CDMA networks 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 individual and global power constraints 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 parameters of the linear receivers. In order to solve the proposed optimization problems efficiently, we develop recursive least squares (RLS) algorithms for adaptive joint iterative power allocation, and receiver and channel parameter estimation. Simulation results show that the proposed algorithms obtain significant gains in performance and capacity over existing schemes.

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arXiv2013

Generalized Reduced-Rank Decompositions Using Switching and Adaptive Algorithms for Space-Time Adaptive Processing

This work presents generalized low-rank signal decompositions with the aid of switching techniques and adaptive algorithms, which do not require eigen-decompositions, for space-time adaptive processing. A generalized scheme is proposed to compute low-rank signal decompositions by imposing suitable constraints on the filtering and by performing iterations between the computed subspace and the low-rank filter. An alternating optimization strategy based on recursive least squares algorithms is presented along with switching and iterations to cost-effectively compute the bases of the decomposition and the low-rank filter. An application to space-time interference suppression in DS-CDMA systems is considered. Simulations show that the proposed scheme and algorithms obtain significant gains in performance over previously reported low-rank schemes.

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arXiv2013

Distributed Space-Time Coding Based on Adjustable Code Matrices for Cooperative MIMO Relaying Systems

An adaptive distributed space-time coding (DSTC) scheme is proposed for two-hop cooperative MIMO networks. Linear minimum mean square error (MMSE) receive filters and adjustable code matrices are considered subject to a power constraint with an amplify-and-forward (AF) cooperation strategy. In the proposed adaptive DSTC scheme, an adjustable code matrix obtained by a feedback channel is employed to transform the space-time coded matrix at the relay node. The effects of the limited feedback and the feedback errors are assessed. Linear MMSE expressions are devised to compute the parameters of the adjustable code matrix and the linear receive filters. Stochastic gradient (SG) and least-squares (LS) algorithms are also developed with reduced computational complexity. An upper bound on the pairwise error probability analysis is derived and indicates the advantage of employing the adjustable code matrices at the relay nodes. An alternative optimization algorithm for the adaptive DSTC scheme is also derived in order to eliminate the need for the feedback. The algorithm provides a fully distributed scheme for the adaptive DSTC at the relay node based on the minimization of the error probability. Simulation results show that the proposed algorithms obtain significant performance gains as compared to existing DSTC schemes.

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arXiv2013

Low-Rank STAP Algorithm for Airborne Radar Based on Basis-Function Approximation

In this paper, we develop a novel reduced-rank space-time adaptive processing (STAP) algorithm based on adaptive basis function approximation (ABFA) for airborne radar applications. The proposed algorithm employs the well-known framework of the side-lobe canceller (SLC) structure and consists of selected sets of basis functions that perform dimensionality reduction and an adaptive reduced-rank filter. Compared to traditional reduced-rank techniques, the proposed scheme works on an instantaneous basis, selecting the best suited set of basis functions at each instant to minimize the squared error. Furthermore, we derive stochastic gradient (SG) and recursive least squares (RLS) algorithm for efficiently implementing the proposed ABFA scheme. Simulations for a clutter-plus-jamming suppression application show that the proposed STAP algorithm outperforms the state-of-the-art reduced-rank schemes in convergence and tracking at significantly lower complexity.

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