Approximate matrix inversion for high-throughput data detection in the large-scale MIMO uplink

Wu, Michael; Yin, Bei; Vosoughi, Aida; Studer, Christoph; Cavallaro, Joseph R.; Dick, Chris

doi:10.1109/iscas.2013.6572301

Cited by 154 publications

(117 citation statements)

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“…In order to overcome the complexity bottleneck of linear data detection methods in large-scale MIMO systems, we recently proposed a low-complexity, approximate inversion method in [5]. However, the impact of the antenna configuration on the performance and hardware complexity of this approximate inversion method has not been analyzed systematically.…”

Section: Low-complexity Data Detectionmentioning

confidence: 99%

“…We show analytically that the approximation error caused by the approximate inversion method of [5] is proportional to the number of users squared and inversely proportional to the number of BS antennas. We then compare the approximate inversion method to a Choleskybased exact inverse and investigated the associated computational complexity.…”

Section: Contributionsmentioning

confidence: 99%

“…Inverting A can result in very high complexity for large-scale MIMO systems as it requires O(M 3 ) operations. Hence, an efficient matrix inversion approximation method was proposed in [5] to arrive at cost-effective hardware implementations. We next summarize the idea behind this approximation method and then provide a corresponding error analysis.…”

Section: Error Analysis Of Approximate Matrix Inversionmentioning

confidence: 99%

“…To reduce the complexity of computing A −1 compared to an exact inversion, we start by the following Neumann series proposed in [5]:…”

Section: K-term Neumann Series Approximationmentioning

confidence: 99%

“…We then compare its implementation with the approximate inversion architecture proposed in [5]. Multiplications Additions…”

Section: Reference Implementation and Complexity Comparisonmentioning

confidence: 99%

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Implementation trade-offs for linear detection in large-scale MIMO systems

Yin

Studer

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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In this paper, we analyze the VLSI implementation tradeoffs for linear data detection in the uplink of large-scale multiple-input multiple-output (MIMO) wireless systems. Specifically, we analyze the error incurred by using the suboptimal, low-complexity matrix inverse proposed in Wu et al., 2013, ISCAS, and compare its performance and complexity to an exact matrix inversion algorithm. We propose a Cholesky-based reference architecture for exact matrix inversion and show corresponding implementation results on an Virtex-7 FPGA. Using this reference design, we perform a performance/complexity trade-off comparison with an FPGA implementation for the proposed approximate matrix inversion, which reveals that the inversion circuit of choice is determined by the antenna configuration (base-station antennas vs. number of users) of large-scale MIMO systems.

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Section: Low-complexity Data Detectionmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Section: Error Analysis Of Approximate Matrix Inversionmentioning

confidence: 99%

“…To reduce the complexity of computing A −1 compared to an exact inversion, we start by the following Neumann series proposed in [5]:…”

Section: K-term Neumann Series Approximationmentioning

confidence: 99%

“…We then compare its implementation with the approximate inversion architecture proposed in [5]. Multiplications Additions…”

Section: Reference Implementation and Complexity Comparisonmentioning

confidence: 99%

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Implementation trade-offs for linear detection in large-scale MIMO systems

Yin

Studer

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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On quasi‐optimum iterative DF detection for large‐scale MU‐MIMO systems

Torres

Gusmão

2017

Trans Emerging Tel Tech

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This paper deals with single‐carrier/frequency‐domain equalization for uplink transmission within a broadband multiuser multi‐input–multi‐output system, where a large number of base station antennas (NR) can be adopted, possibly much larger than the number of transmitter antennas (NT) jointly using the same time‐frequency resource at mobile terminals. In this context, we propose low‐complexity iterative decision‐feedback (DF) detection techniques, which can be an interesting alternative to the usually recommended linear detection techniques. Our performance results for a range of quadrature amplitude modulation schemes are discussed with the help of selected performance bounds, with a special attention being devoted to the convergence of the iterative process and to the impact of an imperfect channel estimation. They confirm that simple linear detection techniques, designed to avoid the need of complex matrix inversions, can lead to unacceptably high error floor levels unless NR≫NT. However, it is shown that the combined use of such simple linear detectors and suitable interference cancellation procedures within the proposed class of iterative DF detection techniques can offer a nonnegligible performance advantage over the somewhat more complex (due to the required matrix inversions) linear minimum mean‐squared error detection technique. Moreover, we can achieve close approximations to the appropriate performance bounds, which are able to take into account channel estimation imperfections, even for NRNT<10, provided that NRNT is also high enough to ensure iterative DF convergence.

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A novel univariate dimension‐reduction based interval finite element method for static response prediction of uncertain structures

Zhao

Feng

et al. 2023

Numerical Meth Engineering

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To eliminate the errors caused by the conventional interval perturbation finite element method due to classic interval arithmetic and neglect of higher-order terms, we propose a novel univariate dimension-reduction based interval finite element method to predict the static response bounds of structures with uncertain but bounded parameters. First, a univariate dimension-reduction algorithm is derived using the generalized Taylor expansion. The global stiffness matrix is expressed as the sum of the median and the univariate disturbance radius. Compared with Taylor expansion approximation, the univariate dimension-reduction approximation has higher accuracy and does not increase the amount of calculation. Then the inverse of the interval global stiffness matrix is approximated as an improved Neumann series. Higher-order terms are included by summing up the geometric terms in the Neumann series. Finally, the improved interval algorithm is used to solve the upper and lower bounds of the structural displacement response and the element stress response. The dependence between the interval parameters is accounted in comparison with the classic interval algorithm. The accuracy and effectiveness of the new method are validated by numerical cases on 2D truss, 3D frame and truck frame with multiple interval parameters.

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Approximate matrix inversion for high-throughput data detection in the large-scale MIMO uplink

Cited by 154 publications

References 10 publications

Implementation trade-offs for linear detection in large-scale MIMO systems

Implementation trade-offs for linear detection in large-scale MIMO systems

On quasi‐optimum iterative DF detection for large‐scale MU‐MIMO systems

A novel univariate dimension‐reduction based interval finite element method for static response prediction of uncertain structures

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