Raimundo Sampaio-Neto

Adaptive Reduced-Rank Processing Based on Joint and Iterative Interpolation, Decimation, and Filtering

We present an adaptive reduced-rank signal processing technique for performing dimensionality reduction in general adaptive filtering problems. The proposed method is based on the concept of joint and iterative interpolation, decimation and filtering. We describe an iterative least squares (LS) procedure to jointly optimize the interpolation, decimation and filtering tasks for reduced-rank adaptive filtering. In order to design the decimation unit, we present the optimal decimation scheme and also propose low-complexity decimation structures. We then develop low-complexity least-mean squares (LMS) and recursive least squares (RLS) algorithms for the proposed scheme along with automatic rank and branch adaptation techniques. An analysis of the convergence properties and issues of the proposed algorithms is carried out and the key features of the optimization problem such as the existence of multiple solutions are discussed. We consider the application of the proposed algorithms to interference suppression in code-division multiple-access (CDMA) systems. Simulations results show that the proposed algorithms outperform the best known reduced-rank schemes with lower complexity.

Reduced-Rank Adaptive Filtering Based on Joint Iterative Optimization of Adaptive Filters

This letter proposes a novel adaptive reduced-rank filtering scheme based on joint iterative optimization of adaptive filters. The novel scheme consists of a joint iterative optimization of a bank of full-rank adaptive filters that forms the projection matrix and an adaptive reduced-rank filter that operates at the output of the bank of filters. We describe minimum mean-squared error (MMSE) expressions for the design of the projection matrix and the reduced-rank filter and low-complexity normalized least-mean squares (NLMS) adaptive algorithms for its efficient implementation. Simulations for an interference suppression application show that the proposed scheme outperforms in convergence and tracking the state-of-the-art reduced-rank schemes at significantly lower complexity.

Reduced-Rank Space–Time Adaptive Interference Suppression With Joint Iterative Least Squares Algorithms for Spread-Spectrum Systems

This paper presents novel adaptive space-time reduced-rank interference-suppression least squares (LS) algorithms based on a joint iterative optimization of parameter vectors. The proposed space-time reduced-rank scheme consists of a joint iterative optimization of a projection matrix that performs dimensionality reduction and an adaptive reduced-rank parameter vector that yields the symbol estimates. The proposed techniques do not require singular value decomposition (SVD) and automatically find the best set of basis for reduced-rank processing. We present LS expressions for the design of the projection matrix and the reduced-rank parameter vector, and we conduct an analysis of the convergence properties of the LS algorithms. We then develop recursive LS (RLS) adaptive algorithms for their computationally efficient estimation and an algorithm that automatically adjusts the rank of the proposed scheme. A convexity analysis of the LS algorithms is carried out along with the development of a proof of convergence for the proposed algorithms. Simulations for a space-time interference suppression application with a direct-sequence code-division multiple-access (DS-CDMA) system show that the proposed scheme outperforms in convergence and tracking the state-of-the-art reduced-rank schemes at a comparable complexity.

Adaptive Reduced-Rank Equalization Algorithms Based on Alternating Optimization Design Techniques for MIMO Systems

This paper presents a novel adaptive reduced-rank multiple-input-multiple-output (MIMO) equalization scheme and algorithms based on alternating optimization design techniques for MIMO spatial multiplexing systems. The proposed reduced-rank equalization structure consists of a joint iterative optimization of the following two equalization stages: 1) a transformation matrix that performs dimensionality reduction and 2) a reduced-rank estimator that retrieves the desired transmitted symbol. The proposed reduced-rank architecture is incorporated into an equalization structure that allows both decision feedback and linear schemes to mitigate the interantenna (IAI) and intersymbol interference (ISI). We develop alternating least squares (LS) expressions for the design of the transformation matrix and the reduced-rank estimator along with computationally efficient alternating recursive least squares (RLS) adaptive estimation algorithms. We then present an algorithm that automatically adjusts the model order of the proposed scheme. An analysis of the LS algorithms is carried out along with sufficient conditions for convergence and a proof of convergence of the proposed algorithms to the reduced-rank Wiener filter. Simulations show that the proposed equalization algorithms outperform the existing reduced- and full- algorithms while requiring a comparable computational cost.

Blind Adaptive Constrained Reduced-Rank Parameter Estimation Based on Constant Modulus Design for CDMA Interference Suppression

This paper proposes a multistage decomposition for blind adaptive parameter estimation in the Krylov subspace with the code-constrained constant modulus (CCM) design criterion. Based on constrained optimization of the constant modulus cost function and utilizing the Lanczos algorithm and Arnoldi-like iterations, a multistage decomposition is developed for blind parameter estimation. A family of computationally efficient blind adaptive reduced-rank stochastic gradient (SG) and recursive least squares (RLS) type algorithms along with an automatic rank selection procedure are also devised and evaluated against existing methods. An analysis of the convergence properties of the method is carried out and convergence conditions for the reduced-rank adaptive algorithms are established. Simulation results consider the application of the proposed techniques to the suppression of multiaccess and intersymbol interference in DS-CDMA systems.

Blind adaptive code-constrained constant modulus algorithms for CDMA interference suppression in multipath channels

A code-constrained constant modulus (CCM) design criterion for linear receivers is investigated for direct sequence code division multiple access (DS-CDMA) in multipath channels based on constrained optimization techniques. A computationally efficient recursive least squares (RLS) type algorithm for jointly estimating the parameters of the channel and the receiver is developed in order to suppress multiaccess (MAI) and inter-symbol interference (ISI). An analysis of the method examines its convergence properties and simulations under nonstationary environments show that the novel algorithms outperform existent techniques.

Adaptive MBER decision feedback multiuser receivers in frequency selective fading channels

In this letter we investigate adaptive minimum bit error rate (BER) decision feedback multiuser receivers for DS-CDMA systems in fast frequency selective Rayleigh fading channels. We examine stochastic gradient adaptive algorithms and introduce fast algorithms for minimizing the BER cost function from training data.

Adaptive reduced-rank MMSE filtering with interpolated FIR filters and adaptive interpolators

In this letter, we propose a broadly applicable reduced-rank filtering approach with adaptive interpolated finite impulse response (FIR) filters in which the interpolator is rendered adaptive. We describe the interpolated minimum mean squared error (MMSE) solution and propose normalized least mean squares (NLMS) and affine-projection (AP) algorithms for both the filter and the interpolator. The resulting filtering structures are considered for equalization and echo cancellation applications. Simulation results showing significant improvements are presented for different scenarios.

Adaptive Interference Suppression for DS-CDMA Systems Based on Interpolated FIR Filters With Adaptive Interpolators in Multipath Channels

In this paper, we propose an adaptive linear-receiver structure based on interpolated finite-impulse response (FIR) filters with adaptive interpolators for direct-sequence code-division multiple-access (DS-CDMA) systems in multipath channels. The interpolated minimum mean-squared error (MMSE) and the interpolated constrained minimum-variance (CMV) solutions are described for a novel scheme, where the interpolator is rendered time-varying in order to mitigate multiple-access interference and multiple-path propagation effects. Based upon the interpolated MMSE and CMV solutions, we present computationally efficient stochastic gradient and exponentially weighted recursive least squares type algorithms for both receiver and interpolator filters in the supervised and blind modes of operation. A convergence analysis of the algorithms and a discussion of the convergence properties of the method are carried out for both modes of operation. Simulation experiments for a downlink scenario show that the proposed structures achieve a superior bit-error-rate convergence and steady-state performance to previously reported reduced-rank receivers at lower complexity.

Low-complexity variable step-size mechanisms for stochastic gradient algorithms in minimum variance CDMA receivers

In this paper, the performance of blind adaptive receivers for direct sequence code division multiple access (DS-CDMA) systems that employ stochastic gradient (SG) algorithms with variable step size mechanisms is investigated. Two low complexity variable step size mechanisms are proposed for estimating the parameters of linear CDMA receivers that operate with SG algorithms. For multipath channels the novel adaptation mechanisms are also incorporated in the channel estimation algorithms, whereas for the single-path case the novel techniques are restricted to the linear receiver parameter vector estimation. Analytical expressions for the excess mean squared error (MSE) are derived and a convergence analysis of the proposed adaptation techniques is carried out for both frequency selective and flat scenarios. Finally, numerical experiments are presented for nonstationary environments, showing that the new mechanisms achieve superior performance to previously reported methods at a reduced complexity