On the matrix inversion approximation based on neumann series in massive MIMO systems

Zhu, Donglin; Li, Boyu; Liang, Ping

doi:10.1109/icc.2015.7248580

Cited by 106 publications

(60 citation statements)

References 10 publications

(61 reference statements)

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“…The output updated inverse matrix after U sequential inflation or deflation operations is designated by Z −1 n,U ∈ C (M ±U )×(M ±U ) . For instance, U = 2 means that Algorithm 1 (or 2) was run twice and its input matrices wereZ The initial number of antennas was set to M = 8 and N = 80, resulting in a ratio β = 10, which guarantees the convergence of (3) with very high probability [12]. Further, the constellation size was set to 64-QAM.…”

Section: Numerical Resultsmentioning

confidence: 99%

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Fast Matrix Inversion Updates for Massive MIMO Detection and Precoding

Rosário

Monteiro

Rodrigues

2016

IEEE Signal Process. Lett.

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In this letter, methods and corresponding complexities for fast matrix inversion updates in the context of massive multiple-input multiple-output (MIMO) are studied. In particular, we propose an on-the-fly method to recompute the zero forcing (ZF) filter when a user is added or removed from the system. Additionally, we evaluate the recalculation of the inverse matrix after a new channel estimation is obtained for a given user. Results are evaluated numerically in terms of bit error rate (BER) using the Neumann series approximation as the initial inverse matrix. It is concluded that, with fewer operations, the performance after an update remains close to the initial one.

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Section: Numerical Resultsmentioning

confidence: 99%

“…Moreover, the lower the eigenvalues, the faster the convergence; which holds true when the ratio β = N/M is high [12].…”

Section: Linear Detection and Neumann Seriesmentioning

confidence: 93%

Fast Matrix Inversion Updates for Massive MIMO Detection and Precoding

Rosário

Monteiro

Rodrigues

2016

IEEE Signal Process. Lett.

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“…when the N=K ratio is not very large). Nevertheless, other matrix inversion approximation approaches always require relatively large N=K ratios to ensure convergence, e.g., N=K [ 5 in [10]. Hence, the joint algorithm can accommodate more single antenna users for a specific BS antenna number, and its application area will be wider.…”

Section: Convergence Analysismentioning

confidence: 99%

Approaches of approximating matrix inversion for zero-forcing pre-coding in downlink massive MIMO systems

Shao

2017

Wireless Netw

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Several approximation approaches including the Gauss-Seidel (GS) method have been proposed to reduce the complexity of matrix inversion for zero-forcing pre-coding in massive multiple-input-multiple-output systems. However, extra computation is required to obtain the matrix inversion from the iteration result of the GS method. In this paper, we propose a new GS-based matrix inversion approximation (GSBMIA) approach. Unlike the traditional GS method, the GSBMIA approach approximates the matrix inversion, which will simplify further calculations. Furthermore, in order to speed up convergence, we propose a joint algorithm based on the GSBMIA and Newton iteration method where the GSBMIA approach is employed to provide an efficient searching direction for the following Newton iterations. Compared with other approximation methods, the joint algorithm can accommodate more single antenna users for the same base station antenna number. Simulation results demonstrate that the joint algorithm and the GSBMIA approach converge faster than the Neumann series and Newton iteration method.

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“…Moreover, H has a fully heterogeneous structure, since it contains L different correlation patterns and link gains. Due to these reasons, we approximate the inverse in (2) with a finite order Neumann series expansion [24,25]. To do this, we separate HH H into its expected diagonal components and correction terms.…”

Section: Introductionmentioning

confidence: 99%

Channel Correlation Diversity in MU-MIMO Systems – Analysis and Measurements

Tataria

Sangodoyin

Molisch

et al. 2019

2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

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In multiuser multiple-input multiple-output (MU-MIMO) systems, channel correlation is detrimental to system performance. We demonstrate that widely used, yet overly simplified, correlation models that generate identical correlation profiles for each terminal tend to severely underestimate the system performance. In sharp contrast, more physically motivated models that capture variations in the power angular spectra across multiple terminals, generate diverse correlation patterns. This has a significant impact on the system performance. Assuming correlated Rayleigh fading and downlink zero-forcing precoding, tight closed-form approximations for the average signalto-noise-ratio, and ergodic sum spectral efficiency are derived. Our expressions provide clear insights into the impact of diverse correlation patterns on the above performance metrics. Unlike previous works, the correlation models are parameterized with measured data from a recent 2.53 GHz urban macrocellular campaign in Cologne, Germany. Overall, results from this paper can be treated as a timely re-calibration of performance expectations from practical MU-MIMO systems.

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On the matrix inversion approximation based on neumann series in massive MIMO systems

Cited by 106 publications

References 10 publications

Fast Matrix Inversion Updates for Massive MIMO Detection and Precoding

Fast Matrix Inversion Updates for Massive MIMO Detection and Precoding

Approaches of approximating matrix inversion for zero-forcing pre-coding in downlink massive MIMO systems

Channel Correlation Diversity in MU-MIMO Systems – Analysis and Measurements

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