On the Asymptotic BER of MMSE Detector in Massive MIMO Systems

Altamirano, Carlos Daniel; Negrete, Juan Carlos; Almeida, Celso de; Garzón, Nathaly Verónica Orozco

doi:10.1007/978-3-030-42531-9_5

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…Linear detectors, i.e., ZF and MMSE [67], [88], [6], [7] • If columns of the propagation matrix are nearly orthogonal, they work properly. • Relatively simple to implement.…”

Section: Overviewmentioning

confidence: 99%

Low Complexity Linear Detectors for Massive MIMO: A Comparative Study

et al. 2021

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Massive multiple-input multiple-output (M-MIMO) is a significant pillar in fifth generation (5G) networks where a large number of antennas is deployed. It provides massive advantages to modern communication systems in data rate, spectral efficiency, number of users serviced simultaneously, energy efficiency, and quality of service (QoS). However, it requires advanced signal processing for data detection. The growing MIMO size leads to complicated scenarios, which makes the detector design a knotty problem. The problem is also becoming more complicated when high-order modulation schemes are exploited and more users are multiplexed. Therefore, it is not practical to employ the maximum likelihood (ML) detector despite the excellent performance. Linear detectors are alternative solutions and relatively simple. Unfortunately, they still need an exact matrix inversion computation, which bears to a significant high complexity. Therefore, several iterative methods are utilized to approximate or evade the matrix inversion rather than computing it. This paper studies the pros and cons of iterative matrix inversion methods where the number of computations and bit-error-rate (BER) are considered to compare between the methods. The comparison is conducted in several scenarios such as different ratio between the number of base station (BS) antennas and user terminal (UT) antennas (β), the number of iterations (n), and the relaxation parameter (ω). This paper also studies the impact of ω in the performance of Richardson (RI) and the successive over-relaxation (SOR) methods. Numerical results show that the conjugate gradient (CG) and optimized coordinate descent (OCD) methods exhibit the lowest complexity with an acceptable performance. In addition, the Gauss-Seidel (GS) method outperforms all other detectors with a trivial complexity increment. It is also noticed that the performance is not improved with every iteration. It is also shown that ω has a great impact and a significant role in achieving a satisfactory performance in both RI and SOR based detectors. From implementation point of view, detectors based on RI, OCD, and CG methods have achieved the highest hardware efficiency (HE) while Jacobi (JA) based detector has obtained the lowest HE. Recent research advances of detection methods are also presented in the open research direction with a potential impact of linear detection methods in initialization and pre-processing.

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“…Linear detectors, i.e., ZF and MMSE [67], [88], [6], [7] • If columns of the propagation matrix are nearly orthogonal, they work properly. • Relatively simple to implement.…”

Section: Overviewmentioning

confidence: 99%

Low Complexity Linear Detectors for Massive MIMO: A Comparative Study

et al. 2021

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“…Unfortunately, the QR decomposition in these nonlinear detectors leads to high computational complexity and low parallelism because of the inclusion of unfavorable matrix operations, such as element elimination. In contrast, suboptimal linear detectors, such as minimum mean square error (MMSE) [5] and zero forcing (ZF) [6], provide a better trade-off between SER and computational complexity, but their complexity still reaches three times the number of transmitting antennas .…”

Section: Introductionmentioning

confidence: 99%

Low-complexity signal detection networks based on Gauss-Seidel iterative method for massive MIMO systems

Yao

et al. 2022

Preprint

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The traditional Gauss-Seidel iterative method runs in parallel with high complexity. To reduce the complexity, this paper proposes a model-driven Deep learning(DL) detector network, namely Block Gauss-Seidel Network(BGS-Net), which is based on the Gauss-Seidel iterative method. We reduce complexity by converting a large matrix inversion to small matrix inversions. Single-antenna user equipment (SAUE) and multiple-antenna user equipment (MAUE) systems under Rayleigh channel are considered in this paper. In order to improve the Symbol Error Ratio(SER) of BGS-Net under MAUE system, we improve the accuracy of the initial solution of BGS-Net, called Improved BGS-Net. The simulation results show that, compared with the existing model-driven algorithms, BGS-Net has lower complexity and similar SER; good robustness, and its performance is less affected by changes in the number of antennas; SER is better than traditional Gauss-Seidel; Improved BGS-Net can improve the SER of BGS-Net.

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“…Nonetheless, the QR decomposition in these nonlinear detection schemes can lead to low parallelism and high computational cost because of the inclusion of matrix operations such as element elimination. Therefore, to cope with the complexity issue, researchers have considered suboptimal linear detection methods, such as the linear minimum-mean-squared error (MMSE) detector [16], which is computationally less expensive, and it has shown good performance for massive MIMO systems, in particular for a favorable propagation environment and a large loading ratio (M/K) [4], where M and K denote the number of receive and transmit antennas, respectively. It is considered near-optimal for massive MIMO and occupies the benchmark place for most linear iterative detectors.…”

Section: Introductionmentioning

confidence: 99%

High-Precision Iterative Preconditioned Gauss–Seidel Detection Algorithm for Massive MIMO Systems

et al. 2022

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Signal detection is a serious challenge for uplink massive multiple-input multiple-output (MIMO) systems. The traditional linear minimum-mean-squared error (MMSE) achieves good detection performance for such systems, but involves matrix inversion, which is computationally expensive due to a large number of antennas. Thus, several iterative methods such as Gauss–Seidel (GS) have been studied to avoid the direct matrix inversion required in the MMSE. In this paper, we improve the GS iteration in order to enhance the detection performance of massive MIMO systems with a large loading factor. By exploiting the property of massive MIMO systems, we introduce a novel initialization strategy to render a quick start for the proposed algorithm. While maintaining the same accuracy of the designed detector, the computing load is further reduced by initialization approximation. In addition, an effective preconditioner is proposed that efficiently transforms the original GS iteration into a new one that has the same solution, but a faster convergence rate than that of the original GS. Numerical results show that the proposed algorithm is superior in terms of complexity and performance than state-of-the-art detectors. Moreover, it exhibits identical error performance to that of the linear MMSE with one-order-less complexity.

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On the Asymptotic BER of MMSE Detector in Massive MIMO Systems

Cited by 5 publications

References 13 publications

Low Complexity Linear Detectors for Massive MIMO: A Comparative Study

Low Complexity Linear Detectors for Massive MIMO: A Comparative Study

Low-complexity signal detection networks based on Gauss-Seidel iterative method for massive MIMO systems

High-Precision Iterative Preconditioned Gauss–Seidel Detection Algorithm for Massive MIMO Systems

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