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
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Proceedings ArticleDOI
Compressed Randomized Utv Decompositions for Low-rank Matrix Approximations in Data Science
TL;DR: In this work, a novel rank-revealing matrix decomposition algorithm termed Compressed Randomized UTV (CoR-UTV) decomposition along with a CoR-utV variant aided by the power method technique is proposed and simulations show that CoR -UTV outperform existing approaches.
Posted Content
Study of Sparsity-Aware Subband Adaptive Filtering Algorithms with Adjustable Penalties.
TL;DR: Two sparsity-aware normalized subband adaptive filter algorithms are proposed by using the gradient descent method to minimize a combination of the original NSAF cost function and the l1-norm penalty function on the filter coefficients, thus improving the performance when identifying sparse systems.
Journal ArticleDOI
Design and Analysis of Polar Codes Based on Piecewise Gaussian Approximation
TL;DR: The Approximate PGA (APGA) that is optimized for medium blocks and provides a performance improvement without increasing complexity is presented and the simplified SPGA (SPGA) as an alternative to the GA, which is optimization for long blocks and achieves high construction accuracy.
Journal ArticleDOI
The Design of a Wavelet-Based Neural Network Adaptive Filter
TL;DR: A wavelet-based neural network adaptive filter based on wavelet transform based on Hopfield neural network is constructed to achieve rapid real-time denoising in adaptive filter LMS.
Posted Content
Study of List-Based OMP and an Enhanced Model for Direction Finding with Non-Uniform Arrays.
TL;DR: In this paper, an enhanced coarray transformation model (EDCTM) and a mixed greedy maximum likelihood algorithm called list-based maximum likelihood Orthogonal Matching Pursuit (LBML-OMP) were proposed for direction-of-arrival estimation with non-uniform linear arrays (NLAs).
References
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Book
Adaptive Filter Theory
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.