QR decomposition

About: QR decomposition is a research topic. Over the lifetime, 3504 publications have been published within this topic receiving 100599 citations. The topic is also known as: QR factorization.

Matrix computations and equation solving using structured networks and training

Abstract: Three structured networks and their corresponding training algorithms are proposed for matrix QR factorization eigenvalue and eigenvector determination, and Lyapunov equation solving. The basic procedure behind these approaches is as follows: represent a given problem by a structured network, train this structured network to match some desired patterns, and obtain the solution to the problem from the weights of the resulting structured network. A general-purpose programmable network architecture is proposed which can be programmed to solve different problems. Simulation results showed that the proposed approaches worked quite well. >

Spatial correlation analysis and model for in-home MIMO power line channels

Abstract: In this paper a statistical characterization of the spatial properties of MIMO PLC channels is provided, where the term spatial refers to the multiple input and output ports. Based on a set of channel measurements in the range from 0 to 100 MHz, the channel covariance matrices and their eigenvalues and eigenvectors are analyzed. Spatial eigenvalues can be approximated by uniform random variables while the eigenvectors can be obtained by QR decomposition of a square matrix with i.i.d. Gaussian samples. Finally the consistency between the measured and the generated spatial correlation model for power line channels is proven. Furthermore, the statistical descriptions extracted from the field measurements are used to update a previously proposed MIMO power line channel model.

Complex radial basis function networks trained by QR-decomposition recursive least square algorithms applied in behavioral modeling of nonlinear power amplifiers

Abstract: In this article, we propose a novel complex radial basis function network approach for dynamic behavioral modeling of nonlinear power amplifier with memory in 3 G systems. The proposed approach utilizes the complex QR-decomposition based recursive least squares (QRD-RLS) algorithm, which is implemented using the complex Givens rotations, to update the weighting matrix of the complex radial basis function (RBF) network. Comparisons with standard least squares algorithms, in batch and recursive process, the QRD-RLS algorithm has the characteristics of good numerical robustness and regular structure, and can significantly improve the complex RBF network modeling accuracy. In this approach, only the signal's complex envelope is used for the model training and validation. The model has been validated using ADS simulated and real measured data. Finally, parallel implementation of the resulting method is briefly discussed. © 2009 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.

A new noise variance and post detection SNR estimation method for MIMO OFDM systems

Abstract: In this paper, a new noise variance and post detection SNR estimation algorithm is proposed for MIMO OFDM systems. The estimated channel vector is employed and multiplied by a special Q matrix which comes from the QR decomposition of a partial frequency domain transform matrix. The noise components in the processed channel estimation vector are used to estimate the noise variance. The post detection SNR can be calculated out with the channel estimation results and the estimated noise variance. Simulation results show that the proposed estimator can obtain accurate estimation results. Moreover, the new estimator is not sensitive to frequency selectivity of wireless channels.