Journal ArticleDOI
Adaptive Reduced-Rank Processing Based on Joint and Iterative Interpolation, Decimation, and Filtering
TLDR
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.read more
Citations
More filters
Journal ArticleDOI
Gradient-based and least-squares-based iterative estimation algorithms for multi-input multi-output systems:
Feng Ding,Yanjun Liu,B. Bao +2 more
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.
Journal ArticleDOI
Reduced-Rank Space–Time Adaptive Interference Suppression With Joint Iterative Least Squares Algorithms for Spread-Spectrum Systems
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.
Journal ArticleDOI
Adaptive Reduced-Rank Equalization Algorithms Based on Alternating Optimization Design Techniques for MIMO Systems
TL;DR: Simulations show that the proposed equalization algorithms outperform the existing reduced- and full- algorithms while requiring a comparable computational cost.
Journal ArticleDOI
Reduced-Rank STAP Schemes for Airborne Radar Based on Switched Joint Interpolation, Decimation and Filtering Algorithm
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.
Journal ArticleDOI
Generalized Design of Low-Complexity Block Diagonalization Type Precoding Algorithms for Multiuser MIMO Systems
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.
References
More filters
Journal ArticleDOI
An iterative algorithm for the computation of the MVDR filter
TL;DR: It is the early, nonasymptotic elements of the generated sequence of estimators that offer favorable bias covariance balance and are seen to outperform in mean-square estimation error, constraint-LMS, RLS-type, orthogonal multistage decomposition, as well as plain and diagonally loaded SMI estimates.
Journal ArticleDOI
The SVD and reduced rank signal processing
TL;DR: This paper derives a number of quantitative rules for reducing the rank of signal models that are used in signal processing algorithms.
Journal ArticleDOI
Reduced-Rank Adaptive Filtering Based on Joint Iterative Optimization of Adaptive Filters
TL;DR: 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.
Journal ArticleDOI
Adaptive reduced-rank MMSE filtering with interpolated FIR filters and adaptive interpolators
TL;DR: The interpolated minimum mean squared error (MMSE) solution is described and the normalized least mean squares (NLMS) and affine-projection (AP) algorithms for both the filter and the interpolator are proposed.