Topic
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.
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01 Nov 2011
TL;DR: This study jointly designs a hardware architecture for QR decomposition and MIMO detection without interface memory buffer, and the channel interpolation is also performed at the same time.
Abstract: This paper presents a configurable MIMO detector with QR decomposition and channel interpolation for 4×4 MIMO-OFDM systems. QR decomposition (QRD) processor and MIMO detector are usually investigated independently in the literature; however, this methodology usually limits operating condition to only slow fading channel. Therefore, this study jointly designs a hardware architecture for QR decomposition and MIMO detection without interface memory buffer, and the channel interpolation is also performed at the same time. This QR-based MIMO detector is implemented in a 90nm CMOS process with 2.02mm2 core area. The chip consumes 56.8mW at 114MHz operating frequency with 1.0V supply and achieves 684Mbps throughput. The proposed chip, which performs one QRD and one MIMO detection for one OFDM symbol, is the first MIMO detector in the literature that supports real-time QR decomposition and MIMO detection for fast fading MIMO-OFDM systems.
15 citations
01 Jan 2003
TL;DR: This paper presents numerically robust learning algorithms based on the QR decomposition — a well known technique in the linear prediction theory.
Abstract: In recent years several neural networks learning algorithms have been developed by making use of the RLS recursion. These algorithms are based on the matrix inversion lemma and in some cases can be numerically ill-conditioned. For example the rounding errors can accumulate and cause errors to occur in both the estimated parameters and the covariance matrix. In this paper we present numerically robust learning algorithms based on the QR decomposition — a well known technique in the linear prediction theory.
15 citations
TL;DR: In this article, the numerical solution of seemingly unrelated regression (SUR) models with vector autoregressive disturbances is considered, and an orthogonal transformation is applied to reduce the model to one with smaller dimensions.
15 citations
01 Jun 1988
TL;DR: Algorithms for solving nonlinear least-squares problems on a message-passing multiprocessor, including an efficient parallel algorithm for determining the Levenberg-Marquardt parameter and a new row-oriented QR factorization algorithm are described.
Abstract: In this paper we describe algorithms for solving nonlinear least-squares problems on a message-passing multiprocessor. We demonstrate new parallel algorithms, including an efficient parallel algorithm for determining the Levenberg-Marquardt parameter and a new row-oriented QR factorization algorithm. Experimental results obtained on an Intel iPSC hypercube are presented and compared with sequential MINPACK code executed on a single processor. These experimental results show that essentially full efficiency is obtained for problems where the row size is sufficiently larger than the number of processors. These algorithms have the advantage of involving only simple data movements and consequently are not constrained to the hypercube architecture.
15 citations
TL;DR: In this paper, the authors consider refined implementations of algorithms based on Householder and Givens transformations to find the Q-factor in the QR-factorization of a matrix solution of linear time dependent differential systems.
15 citations