Fast Matrix Inversion Updates for Massive MIMO Detection and Precoding

Rosário, Francisco; Monteiro, Francisco A.; Rodrigues, António

doi:10.1109/lsp.2015.2500682

Cited by 46 publications

(41 citation statements)

References 15 publications

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“…For example, the sum rate achieved by ZF pre-coding involves the trace of W À1 [5], so in order to obtain the sum rate, we have to compute W À1 . Fast matrix inversion updates was proposed in [6] to update the matrix inversion without re-computing W À1 when a user was added to or removed from the system. However, the proposed method requires to calculate W À1 at least once, otherwise the update is impossible.…”

Section: System Modelmentioning

confidence: 99%

“…What they approximate is not the matrix inversion but a product containing the matrix inversion. However, the matrix inversion is required in some relevant computations, such as fast matrix inversion updates [6] and the sum rate calculation [5]. Thus the matrix inversion must be separated from the iteration result of the three approaches, which brings about complicated calculations.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

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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“…In massive MIMO systems, linear detectors achieve near-optimal performance by exploiting the channel hardening effect [10], and approximate matrix inversions via Neumann series approximations [11] are used for practical implementations. However, large MIMO systems do not have very large receive-to-transmit antenna ratios.…”

Section: Introductionmentioning

confidence: 99%

Large MIMO Detection Schemes Based on Channel Puncturing: Performance and Complexity Analysis

Sarieddeen

Mansour

Chehab

2018

IEEE Trans. Commun.

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A family of low-complexity detection schemes based on channel matrix puncturing targeted for large multiple-input multiple-output (MIMO) systems is proposed. It is well-known that the computational cost of MIMO detection based on QR decomposition is directly proportional to the number of nonzero entries involved in back-substitution and slicing operations in the triangularized channel matrix, which can be too high for low-latency applications involving large MIMO dimensions. By systematically puncturing the channel to have a specific structure, it is demonstrated that the detection process can be accelerated by employing standard schemes such as chase detection, list detection, nulling-andcancellation detection, and sub-space detection on the transformed matrix. The performance of these schemes is characterized and analyzed mathematically, and bounds on the achievable diversity gain and probability of bit error are derived. Surprisingly, it is shown that puncturing does not negatively impact the receive diversity gain in hard-output detectors. The analysis is extended to soft-output detection when computing per-layer bit log-likelihood ratios; it is shown that significant performance gains are attainable by ordering the layer of interest to be at the root when puncturing the channel. Simulations of coded and uncoded scenarios certify that the proposed schemes scale up efficiently both in the number of antennas and constellation size, as well as in the presence of correlated channels. In particular, softoutput per-layer sub-space detection is shown to achieve a 2.5 dB SNR gain at 10 −4 bit error rate in 256-QAM 16×16 MIMO, while saving 77% of nulling-and-cancellation computations.

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“…Several complexity reduction strategies have been proposed as alternatives to the closed-form MMSE filter, including: Gauss-Seidel optimization [3], Neumann series approximation [4], and the symmetric successive overrelaxation method [5]. An approach that exploits the array manifold separability has been presented as a solution to reduce the computational complexity in large array beamforming tasks [6].…”

Section: Introductionmentioning

confidence: 99%

A low-complexity equalizer for massive MIMO systems based on array separability

Ribeiro

Schwarz

Rupp

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper, we propose a low-complexity equalizer for multiple-input multiple-output systems with large receive antenna arrays. The computational complexity reduction is achieved by exploiting array separability on a geometric channel model. This property suggests a two-stage receive processing, consisting of (i) sub-array beamforming and (ii) low-dimension minimum mean square error (MMSE) equalization. Simulations indicate that the proposed method outperforms the classical MMSE filter in terms of complexity provided that the number of channel scatterers and the sub-arrays dimensions are not excessively large.

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Fast Matrix Inversion Updates for Massive MIMO Detection and Precoding

Cited by 46 publications

References 15 publications

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

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

Large MIMO Detection Schemes Based on Channel Puncturing: Performance and Complexity Analysis

A low-complexity equalizer for massive MIMO systems based on array separability

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