Journal ArticleDOI
SINR Analysis of the Subtraction-Based SMI Beamformer
Reads0
Chats0
TLDR
Simulations show that the derived approximations of the expected value of the signal-to-interference-plus-noise ratio (SINR) are close enough to represent the true values of the SINR, when the sample size is small and the arrival direction mismatch exists.Abstract:
The sample matrix inversion (SMI) beamformer suffers from performance degradation due to the finite sample size effect and the arrival angle mismatch problem. A simple technique to provide robustness to the conventional SMI beamformer is to block the desired signal from the received data before calculating the beamformer's weight vector, which leads to the subtraction-based SMI (S-SMI) beamformer. In this correspondence, closed-form approximations of the expected value of the signal-to-interference-plus-noise ratio (SINR) for the S-SMI beamformer and the SMI beamformer are derived, where the effect of both finite sample size and arrival angle mismatch are considered. Simulations show that the derived approximations are close enough to represent the true values of the SINR, when the sample size is small and the arrival direction mismatch exists.read more
Citations
More filters
Journal ArticleDOI
Average SINR Calculation of a Persymmetric Sample Matrix Inversion Beamformer
TL;DR: Simulation results reveal that the exploitation of the persymmetric structure is equivalent to doubling the amount of training data, and thus the SINR loss of the Persymmetric SMI beamformer can be significantly reduced and can work in the case of limited training data where the traditional beamformers cannot work.
Journal ArticleDOI
Introduction to multivariate analysis, by C. Chatfield and A. J. Collins. Pp 246. £13 hardcover, £7·50 paperback. 1980. ISBN 0-412-16030-7/4 (Chapman and Hall)
TL;DR: In this paper, the multivariate normal distribution is used for principal component analysis and multivariate analysis of covariance and related topics, as well as multi-dimensional scaling and cluster analysis.
Proceedings ArticleDOI
On the performance of cell-free massive MIMO with short-term power constraints
TL;DR: In this paper, a normalized conjugate beamforming scheme, which satisfies short-term average power constraints at the APs, is proposed and analyzed taking into account the effect of imperfect channel information.
Journal ArticleDOI
Performance Analysis for Finite Sample MVDR Beamformer With Forward Backward Processing
Lei Zhang,Wei Liu,Lei Yu +2 more
TL;DR: The employment of forward-backward (FB) processing to both batch and adaptive beamformers provides a superior performance than the forward only (FO) algorithm.
Proceedings ArticleDOI
Performance and analysis of downlink multiuser MIMO systems with regularized zero-forcing precoding in Ricean fading channels
TL;DR: This model caters to the presence of a unique Rice factor for each user terminal, making it suitable for analysis of modern systems, such as small cells and millimeter-wave, and is seen to remain tight across the signal-to-noise-ratio range considered.
References
More filters
Journal ArticleDOI
Rapid Convergence Rate in Adaptive Arrays
TL;DR: A direct method of adaptive weight computation, based on a sample covariance matrix of the noise field, has been found to provide very rapid convergence in all cases, i.e., independent of the eigenvalue distribution.
Book
Introduction to multivariate analysis
TL;DR: In this article, the multivariate normal distribution is used for principal component analysis and multivariate analysis of covariance and related topics, as well as multi-dimensional scaling and cluster analysis.
Journal ArticleDOI
Robust adaptive beamforming
TL;DR: It is shown that a simple scaling of the projection of tentative weights, in the subspace orthogonal to the linear constraints, can be used to satisfy the quadratic inequality constraint.
Journal ArticleDOI
Robust adaptive beamforming using worst-case performance optimization: a solution to the signal mismatch problem
TL;DR: A new approach to robust adaptive beamforming in the presence of an arbitrary unknown signal steering vector mismatch is developed based on the optimization of worst-case performance.
Journal ArticleDOI
Covariance matrix estimation errors and diagonal loading in adaptive arrays
TL;DR: In this paper, the effect of covariance matrix sample size on the system performance of adaptive arrays using the sample matrix inversion (SMI) algorithm has been investigated, and a technique to reduce these effects by modifying the covariance matrices estimate is described from the point of view of eigenvector decomposition.