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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.


Papers
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Journal ArticleDOI
TL;DR: The singular value and QR decompositions of the projection matrix give new types of series expansions of the plasma image and present stable and high speed imaging which might be useful for monitoring the time evolution of plasma in a fusion device with angular-limited, sparse and noisy data in projection observation.

14 citations

Journal ArticleDOI
TL;DR: A new precoder that reduces the feedback overhead in an orthogonal frequency-division multiplexing (OFDM)-based relay network is proposed and closed-form expressions for the link achievable average sum-rate and the outage probability can be derived and then their accuracies verified via Monte Carlo simulations.
Abstract: A new precoder that reduces the feedback overhead in an orthogonal frequency-division multiplexing (OFDM)-based relay network is proposed. A joint singular value decomposition (SVD) and QR decomposition is employed on the source-destination link and the source-relay-destination link. Using the proposed precoder, only one precoding matrix needs to be fed back, independent of the total number of subcarriers. Kernel density estimation is applied to determine approximate forms of unknown distributions of the end-to-end effective channel powers obtained by this precoder. Using approximated probability density functions (pdfs) for these effective channel powers, closed-form expressions for the link achievable average sum-rate and the outage probability can be derived and then their accuracies verified via Monte Carlo simulations. The achievable sum-rates are also compared with that of SVD-based precoders and for varying codebook size.

14 citations

Proceedings ArticleDOI
21 Mar 2008
TL;DR: An unbiased MMSE metric is introduced that can be applied to existing MIMO detectors in order to improve their performance and the resulting real-time detector has been compared to state-of-the-art detectors previously implemented, in terms of complexity, error performance and throughput.
Abstract: We consider the problem of detecting a vector signal transmitted over a multiple input-multiple output (MIMO) channel. A number of suboptimal detectors have been proposed to solve that problem, given that maximum likelihood (ML) detection is NP-hard. After reviewing the main concepts of the ML and the minimum mean square error (MMSE) metrics, we introduce an unbiased MMSE metric that can be applied to existing MIMO detectors in order to improve their performance. Applying the biased and unbiased MMSE metrics together with a real-valued representation of the system, the performance and complexity of a number suboptimal MIMO detectors is compared in this paper, showing how the QR decomposition-M (QRD-M) can be used to approximate ML performance with low complexity. In order to further validate those results, the QRD-M algorithm has been implemented on a field-programmable gate array (FPGA) platform, showing an excellent fixed-point performance under real-time conditions. Finally, the resulting real-time detector has been compared to state-of-the-art detectors previously implemented, in terms of complexity, error performance and throughput.

14 citations

Journal ArticleDOI
TL;DR: This paper develops a low-complexity QRD algorithm based on fast plane rotations, which does not require square-root operations for decomposing a complex-valued matrix.
Abstract: QR decomposition (QRD) is widely used in various engineering applications and its implementation has a significant impact on the system performance and complexity. This paper develops a low-complexity QRD algorithm based on fast plane rotations, which does not require square-root operations for decomposing a complex-valued matrix. Furthermore, an update-based implementation is presented where computations are performed incrementally as the data arrives sequentially in time to drastically reduce the overall latency and hardware resources. Practical results for QRD-based spatial correlation estimator are provided to demonstrate the effectiveness of our solution for multiple-input multiple-output (MIMO) systems with complex-valued signals.

14 citations

Proceedings ArticleDOI
15 Apr 2007
TL;DR: A new algorithm for background subtraction that can model the background image from a sequence of images, even if there are foreground objects in each image frame, and identification of the background based on QR-decomposition method in linear algebra is presented.
Abstract: This paper presents a new algorithm for background subtraction that can model the background image from a sequence of images, even if there are foreground objects in each image frame. In contrast with Gaussian mixture model algorithm, in our proposed method the problem of distinguishing between background and foreground kernels becomes trivial. The key idea of our method lies in the identification of the background based on QR-decomposition method in linear algebra. R-values taken from QR-decomposition can be applied to decompose a given system to indicate the degree of the significance of the decomposed parts. We split the image into small blocks and select the background blocks with the weakest contribution, according to the assigned R-values. Simulation results show the better background detection performance with respect to some others.

14 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202331
202273
202190
2020132
2019126
2018139