1,568 research outputs found

    Blind adaptive constrained reduced-rank parameter estimation based on constant modulus design for CDMA interference suppression

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    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

    Low-Complexity Reduced-Rank Beamforming Algorithms

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    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.Comment: 7 figure

    A Stieltjes transform approach for analyzing the RLS adaptive Filter

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    Although the RLS filter is well-known and various algorithms have been developed for its implementation, analyzing its performance when the regressors are random, as is often the case, has proven to be a formidable task. The reason is that the Riccati recursion, which propagates the error covariance matrix, becomes a random recursion. The existing results are approximations based on assumptions that are often not very realistic. In this paper we use ideas from the theory of large random matrices to find the asymptotic (in time) eigendistribution of the error covariance matrix of the RLS filter. Under the assumption of a large dimensional state vector (in most cases n = 10-20 is large enough to get quite accurate predictions) we find the asymptotic eigendistribution of the error covariance for temporally white regressors, shift structured regressors, and for the RLS filter with intermittent observations

    Adaptive interference suppression for DS-CDMA systems based on interpolated FIR filters with adaptive interpolators in multipath channels

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    In this work 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 (MAI) and multiple-path propagation effects. Based upon the interpolated MMSE and CMV solutions we present computationally efficient stochastic gradient (SG) and exponentially weighted recursive least squares type (RLS) 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 BER convergence and steady-state performance to previously reported reduced-rank receivers at lower complexity
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