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Journal ArticleDOI

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

TL;DR: An iterative least squares (LS) procedure to jointly optimize the interpolation, decimation and filtering tasks for reduced-rank adaptive filtering for interference suppression in code-division multiple-access (CDMA) systems is described.
Abstract: 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.
Citations
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Journal ArticleDOI
01 Jan 2012
TL;DR: A gradient-based and a least-squares-based iterative estimation algorithms to estimate the parameters for a multi-input multi-output (MIMO) system with coloured auto-regressive moving average (ARMA) noise from input–output data, based on the gradient search and least-Squares principles, respectively are developed.
Abstract: System modelling is important for studying the motion laws of dynamical systems. Parameters of the system models can be estimated through identification methods from measurement data. This paper develops a gradient-based and a least-squares-based iterative estimation algorithms to estimate the parameters for a multi-input multi-output (MIMO) system with coloured auto-regressive moving average (ARMA) noise from input–output data, based on the gradient search and least-squares principles, respectively. The key is to replace the unknown noise terms and residuals contained in the information vector with their corresponding estimates at the previous iteration. The simulation test results indicate that the proposed algorithms are effective.

224 citations

Journal ArticleDOI
TL;DR: 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.
Abstract: 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.

183 citations


Additional excerpts

  • ...Reduced-rank STAP techniques [15]–[40] are powerful and effective approaches in low-sample support situations and in problems with large filters....

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Journal ArticleDOI
TL;DR: Simulations show that the proposed equalization algorithms outperform the existing reduced- and full- algorithms while requiring a comparable computational cost.
Abstract: 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.

181 citations


Cites background or methods from "Adaptive Reduced-Rank Processing Ba..."

  • ...data onto a low-rank subspace associated with the signals of interest, reduced-rank methods can eliminate the interference that lies in the noise subspace and perform denoising [20]–[34]....

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  • ...Reduced-rank techniques [20]–[34] are powerful and effective approaches in low-sample support situations and in problems with large filters....

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Journal ArticleDOI
TL;DR: The simulation results show that the proposed RR-SJIDF STAP schemes with both the RLS and the CCG algorithms converge at a very fast speed and provide a considerable SINR improvement over the state-of-the-art reduced-rank schemes.
Abstract: In this paper, we propose a reduced-rank space-time adaptive processing (STAP) technique for airborne phased array radar applications. The proposed STAP method performs dimensionality reduction by using a reduced-rank switched joint interpolation, decimation and filtering algorithm (RR-SJIDF). In this scheme, a multiple-processing-branch (MPB) framework, which contains a set of jointly optimized interpolation, decimation and filtering units, is proposed to adaptively process the observations and suppress jammers and clutter. The output is switched to the branch with the best performance according to the minimum variance criterion. In order to design the decimation unit, we present an optimal decimation scheme and a low-complexity decimation scheme. We also develop two adaptive implementations for the proposed scheme, one based on a recursive least squares (RLS) algorithm and the other on a constrained conjugate gradient (CCG) algorithm. The proposed adaptive algorithms are tested with simulated radar data. The simulation results show that the proposed RR-SJIDF STAP schemes with both the RLS and the CCG algorithms converge at a very fast speed and provide a considerable SINR improvement over the state-of-the-art reduced-rank schemes.

172 citations


Cites background or methods or result from "Adaptive Reduced-Rank Processing Ba..."

  • ...Recently, reduced-rank adaptive processing algorithms based on joint iterative optimization of adaptive filters [25], [26] and based on an adaptive diversity-combined decimation and interpolation scheme [27]–[ 31 ] were proposed, respectively....

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  • ...Compared with the scheme in [ 31 ], the proposed scheme, which employs multiple pairs of interpolators and reduced-rank filters, can provide improved performance....

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  • ...The reduced-rank adaptive filtering scheme based on combined decimation and interpolation filtering was presented in [30] and [ 31 ]....

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  • ...Unlike the previous work in [ 31 ], multiple interpolators and reduced-rank filters are employed in the MPB framework and are designed with the minimum variance distortionless response (MVDR) criterion....

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  • ...In [ 31 ], the joint interpolation, decimation and filtering (JIDF) algorithm, which employs one pair of a interpolator and a reduced-rank filter together with a group of decimation units, provides a significant improvement in terms of convergence and SINR performance for code-division multiple-access (CDMA) applications....

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Journal ArticleDOI
TL;DR: Analytical and simulation results show that the proposed precoding algorithms can achieve a comparable sum-rate performance as BD-type precode algorithms, substantial bit error rate (BER) performance gains, and a simplified receiver structure, while requiring a much lower complexity.
Abstract: 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.

158 citations

References
More filters
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34,729 citations

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01 Jan 1986
TL;DR: In this paper, the authors propose a recursive least square adaptive filter (RLF) based on the Kalman filter, which is used as the unifying base for RLS Filters.
Abstract: Background and Overview. 1. Stochastic Processes and Models. 2. Wiener Filters. 3. Linear Prediction. 4. Method of Steepest Descent. 5. Least-Mean-Square Adaptive Filters. 6. Normalized Least-Mean-Square Adaptive Filters. 7. Transform-Domain and Sub-Band Adaptive Filters. 8. Method of Least Squares. 9. Recursive Least-Square Adaptive Filters. 10. Kalman Filters as the Unifying Bases for RLS Filters. 11. Square-Root Adaptive Filters. 12. Order-Recursive Adaptive Filters. 13. Finite-Precision Effects. 14. Tracking of Time-Varying Systems. 15. Adaptive Filters Using Infinite-Duration Impulse Response Structures. 16. Blind Deconvolution. 17. Back-Propagation Learning. Epilogue. Appendix A. Complex Variables. Appendix B. Differentiation with Respect to a Vector. Appendix C. Method of Lagrange Multipliers. Appendix D. Estimation Theory. Appendix E. Eigenanalysis. Appendix F. Rotations and Reflections. Appendix G. Complex Wishart Distribution. Glossary. Abbreviations. Principal Symbols. Bibliography. Index.

16,062 citations

Book
01 Jan 1995

12,671 citations

Book
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9,038 citations

Book
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8,608 citations


"Adaptive Reduced-Rank Processing Ba..." refers methods in this paper

  • ...where the matrix with the samples of has a Hankel structure [27] and is described by...

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