arXiv subjects

R. C. de Lamare

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

172 records · Page 10Linked to original sources

arXiv2013

Adaptive Randomized Distributed Space-Time Coding in Cooperative MIMO Relay Systems

An adaptive randomized distributed space-time coding (DSTC) scheme and algorithms are proposed for two-hop cooperative MIMO networks. Linear minimum mean square error (MMSE) receivers and an amplify-and-forward (AF) cooperation strategy are considered. In the proposed DSTC scheme, a randomized matrix obtained by a feedback channel is employed to transform the space-time coded matrix at the relay node. Linear MMSE expressions are devised to compute the parameters of the adaptive randomized matrix and the linear receive filter. A stochastic gradient algorithm is also developed to compute the parameters of the adaptive randomized matrix with reduced computational complexity. We also derive the upper bound of the error probability of a cooperative MIMO system employing the randomized space-time coding scheme first. The simulation results show that the proposed algorithms obtain significant performance gains as compared to existing DSTC schemes.

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arXiv2013

Adaptive Distributed Space-Time Coding in Cooperative MIMO Relaying Systems using Limited Feedback

An adaptive randomized distributed space-time coding (DSTC) scheme is proposed for two-hop cooperative MIMO networks. Linear minimum mean square error (MMSE) receiver filters and randomized matrices subject to a power constraint are considered with an amplify-and-forward (AF) cooperation strategy. In the proposed DSTC scheme, a randomized matrix obtained by a feedback channel is employed to transform the space-time coded matrix at the relay node. The effect of the limited feedback and feedback errors are considered. Linear MMSE expressions are devised to compute the parameters of the adaptive randomized matrix and the linear receive filters. A stochastic gradient algorithm is also developed with reduced computational complexity. The simulation results show that the proposed algorithms obtain significant performance gains as compared to existing DSTC schemes.

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arXiv2013

Joint Power Adjustment and Receiver Design for Distributed Space-Time Coded in Cooperative MIMO Systems

In this paper, a joint power allocation algorithm with minimum mean-squared error (MMSE) receiver for a cooperative Multiple-Input and Multiple-Output (MIMO) network which employs multiple relays and a Decode-and-Forward (DF) strategy is proposed. A Distributed Space-Time Coding (DSTC) scheme is applied in each relay node. We present a joint constrained optimization algorithm to determine the power allocation parameters and the MMSE receive filter parameter vectors for each transmitted symbol in each link, as well as the channel coefficients matrix. A Stochastic Gradient (SG) algorithm is derived for the calculation of the joint optimization in order to release the receiver from the massive calculation complexity for the MMSE receive filter and power allocation parameters. The simulation results indicate that the proposed algorithm obtains gains compared to the equal power allocation system.

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arXiv2013

Adaptive Reduced-Rank MBER Linear Receive Processing for Large Multiuser MIMO Systems

In this work, we propose a novel adaptive reduced-rank strategy based on joint interpolation, decimation and filtering (JIDF) for large multiuser multiple-input multiple-output (MIMO) systems. In this scheme, a reduced-rank framework is proposed for linear receive processing and multiuser interference suppression according to the minimization of the bit error rate (BER) cost function. We present a structure with multiple processing branches that performs dimensionality reduction, where each branch contains a group of jointly optimized interpolation and decimation units, followed by a linear receive filter. We then develop stochastic gradient (SG) algorithms to compute the parameters of the interpolation and receive filters along with a low-complexity decimation technique. Simulation results are presented for time-varying environments and show that the proposed MBER-JIDF receive processing strategy and algorithms achieve a superior performance to existing methods at a reduced complexity.

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arXiv2013

Low-Complexity Channel Estimation with Set-Membership Algorithms for Cooperative Wireless Sensor Networks

In this paper, we consider a general cooperative wireless sensor network (WSN) with multiple hops and the problem of channel estimation. Two matrix-based set-membership algorithms are developed for the estimation of the complex matrix channel parameters. The main goal is to reduce the computational complexity significantly as compared with existing channel estimators and extend the lifetime of the WSN by reducing its power consumption. The first proposed algorithm is the set-membership normalized least mean squares (SM-NLMS) algorithm. The second is the set-membership recursive least squares (RLS) algorithm called BEACON. Then, we present and incorporate an error bound function into the two channel estimation methods which can adjust the error bound automatically with the update of the channel estimates. Steady-state analysis in the output mean-squared error (MSE) are presented and closed-form formulae for the excess MSE and the probability of update in each recursion are provided. Computer simulations show good performance of our proposed algorithms in terms of convergence speed, steady state mean square error and bit error rate (BER) and demonstrate reduced complexity and robustness against the time-varying environments and different signal-to-noise ratio (SNR) values.

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arXiv2013

Joint Maximum Sum-Rate Receiver Design and Power Adjustment for Multihop Wireless Sensor Networks

In this paper, we consider a multihop wireless sensor network (WSN) with multiple relay nodes for each hop where the amplify-and-forward (AF) scheme is employed. We present a strategy to jointly design the linear receiver and the power allocation parameters via an alternating optimization approach that maximizes the sum rate of the WSN. We derive constrained maximum sum-rate (MSR) expressions along with an algorithm to compute the linear receiver and the power allocation parameters with the optimal complex amplification coefficients for each relay node. Computer simulations show good performance of our proposed methods in terms of sum rate compared to the method with equal power allocation.

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arXiv2013

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.

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arXiv2013

Low-Complexity Reduced-Rank Beamforming Algorithms

A reduced-rank framework with set-membership filtering (SMF) techniques is presented for adaptive beamforming problems encountered in radar systems. We develop and analyze stochastic gradient (SG) and recursive least squares (RLS)-type adaptive algorithms, which achieve an enhanced convergence and tracking performance with low computational cost as compared to existing techniques. Simulations show that the proposed algorithms have a superior performance to prior methods, while the complexity is lower.

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arXiv2013

Set-Membership Constrained Conjugate Gradient Beamforming Algorithms

In this work a constrained adaptive filtering strategy based on conjugate gradient (CG) and set-membership (SM) techniques is presented for adaptive beamforming. A constraint on the magnitude of the array output is imposed to derive an adaptive algorithm that performs data-selective updates when calculating the beamformer's parameters. We consider a linearly constrained minimum variance (LCMV) optimization problem with the bounded constraint based on this strategy and propose a CG type algorithm for implementation. The proposed algorithm has data-selective updates, a variable forgetting factor and performs one iteration per update to reduce the computational complexity. The updated parameters construct a space of feasible solutions that enforce the constraints. We also introduce two time-varying bounding schemes to measure the quality of the parameters that could be included in the parameter space. A comprehensive complexity and performance analysis between the proposed and existing algorithms are provided. Simulations are performed to show the enhanced convergence and tracking performance of the proposed algorithm as compared to existing techniques.

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arXiv2012

Adaptive Reduced-Rank LCMV Beamforming Algorithms Based on Joint Iterative Optimization of Filters: Design and Analysis

This paper presents reduced-rank linearly constrained minimum variance (LCMV) beamforming algorithms based on joint iterative optimization of filters. The proposed reduced-rank scheme is based on a constrained joint iterative optimization of filters according to the minimum variance criterion. The proposed optimization procedure adjusts the parameters of a projection matrix and an adaptive reducedrank filter that operates at the output of the bank of filters. We describe LCMV expressions for the design of the projection matrix and the reduced-rank filter. We then describe stochastic gradient and develop recursive least-squares adaptive algorithms for their efficient implementation along with automatic rank selection techniques. An analysis of the stability and the convergence properties of the proposed algorithms is presented and semi-analytical expressions are derived for predicting their mean squared error (MSE) performance. Simulations for a beamforming application show that the proposed scheme and algorithms outperform in convergence and tracking the existing full-rank and reduced-rank algorithms while requiring comparable complexity

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