QR Decomposition-Based Matrix Inversion for High Performance Embedded MIMO Receivers

Ma, Lei; Dickson, K.; McAllister, John; McCanny, J.V.

doi:10.1109/tsp.2011.2105485

Cited by 85 publications

(41 citation statements)

References 18 publications

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“…We do not consider the Gram Schmidt method here for it requires costly operations, such as square root operations and divisions, and it is not numerically stable; see [17] and references therein.…”

Section: Computational Overhead For the Modified Pacmentioning

confidence: 99%

Circular Sphere Decoding: A Low Complexity Detection for MIMO Systems With General Two-dimensional Signal Constellations

Jang¹,

Nooshabadi²,

Kim³

et al. 2017

IEEE Trans. Veh. Technol.

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Abstract-We propose a low complexity complex valued Sphere Decoding (CV-SD) algorithm, referred to as Circular Sphere Decoding (CSD) which is applicable to multiple-input multiple-output (MIMO) systems with arbitrary two dimensional (2D) constellations. CSD provides a new constraint test. This constraint test is carefully designed so that the element-wise dependency is removed in the metric computation for the test. As a result, the constraint test becomes simple to perform without restriction on its constellation structure. By additionally employing this simple test as a prescreening test, CSD reduces the complexity of the CV-SD search. We show that the complexity reduction is significant while its maximum-likelihood (ML) performance is not compromised. We also provide a powerful tool to estimate the pruning capacity of any particular search tree. Using this tool, we propose the Predict-And-Change strategy which leads to a further complexity reduction in CSD. Extension of the proposed methods to soft output SD is also presented.Index Terms-multiple input multiple output (MIMO), circular sphere decoding (CSD), predict and change (PAC), sphere decoding (SD), complex-valued, arbitrary constellation

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Section: Computational Overhead For the Modified Pacmentioning

confidence: 99%

Circular Sphere Decoding: A Low Complexity Detection for MIMO Systems With General Two-dimensional Signal Constellations

Jang¹,

Nooshabadi²,

Kim³

et al. 2017

IEEE Trans. Veh. Technol.

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“…QR decomposition is an elementary operation, which decomposes a matrix into an orthogonal and a triangular matrix. QR decomposition of a real square matrix A is a decomposition of A as A = Q×R, where Q is an orthogonal matrix (Q T × Q = I) and R is an upper triangular matrix [5][6]. And we can factor m x n matrices (with m ≥ n) of full rank as the product of an m x n orthogonal matrix where QT × Q = I and an n x n upper triangular matrix.…”

Section: Qr Decompositionmentioning

confidence: 99%

Digital Predistortion of Power Amplifier using Quadratic Rotation Method

Goyal¹

2017

ijecs

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Abstract---In this paper, the task of design and implementation of digital pre-distortion technique has been divided into two parts. The behavioural model of power amplifier has been developed and digital predistorters module has been developed. AM-AM characterstics for the proposed model is presented and power spectral density (PSD) have been considered as the major parameters to check the performance and modelling accuracy. Parameter estimation and their automatic adjustment is a key issue in adaptive digital predistorter design.Keywords---Adjacent channel leakage ratio, error vector magnitude, power spectral density, Quadrature Rotation.

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“…The complexity of the direct analytic method increases exponentially with the size of matrix, so matrix decom-position becomes the most common method applied in matrix inversion with large dimensions, such as LU decomposition (with partial pivoting) [4,5,6,7], QR decomposition [8,9,10], Cholesky decomposition [11], etc. By analyzing these algorithms [12], LU decomposition (with partial pivoting) has better generality than Cholesky decomposition which only applies to symmetric positive definite matrices, and lower computational complexity than QR decomposition.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of high performance matrix inversion based on reconfigurable processor

Wang

Feng

et al. 2016

IEICE Electron. Express

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In this paper, we propose a high performance matrix inversion implementation on a reconfigurable application specific processor. Our implementation can accelerate variable order matrix inversion ranging from 4 to 144. We adopt LU decomposition to reduce the computation complexity and a pivoting operation to ensure the stability. In order to get higher performance within the limited resources, parallel computing and timesharing multiplexing are employed. The chip testing results show that our implementation improve the performance of inversion efficiently. The highest parallel speed-up ratio can achieve 3 times, and the execution time of a 144 × 144 matrix inversion is 4.07 ms.

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QR Decomposition-Based Matrix Inversion for High Performance Embedded MIMO Receivers

Cited by 85 publications

References 18 publications

Circular Sphere Decoding: A Low Complexity Detection for MIMO Systems With General Two-dimensional Signal Constellations

Circular Sphere Decoding: A Low Complexity Detection for MIMO Systems With General Two-dimensional Signal Constellations

Digital Predistortion of Power Amplifier using Quadratic Rotation Method

Design and implementation of high performance matrix inversion based on reconfigurable processor

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