BER Evaluation of Linear Detectors in Massive MIMO Systems Under Imperfect Channel Estimation Effects

Altamirano, Carlos Daniel; Negrete, Juan Carlos; Mora, Henry Ramiro Carvajal; Almeida, Celso de

doi:10.1109/access.2019.2956828

Cited by 23 publications

(30 citation statements)

References 38 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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“…The most common used LD in MMIMO channels are Zero-Forcing (ZF) and Minimum Mean-Square Error (MMSE). As for the Matched-Filter (MF), which is the simplest detector in terms of complexity, is efficient only for BPSK modulation when N R = N T [20], [21]. Under these circumstances, MF might be able to replace ZF estimate both in the SD algorithm and in the proposed IR method.…”

Section: Introductionmentioning

confidence: 99%

Hardware Implementation and Performance Analysis of Improved Sphere Decoder in Spatially Correlated Massive MIMO Channels

Vordonis¹,

Paliouras²

2021

Preprint

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<div>Detection for high-dimensional multiple-input multiple-output (MIMO) and Massive MIMO (MMIMO) systems is an active field of research in wireless communications. While most works consider spatially uncorrelated channels, practical MMIMO channels are correlated. This paper investigates the impact of correlation on Sphere Decoder (SD), not only for Single-User (SU) but also for Multi-User (MU) scenarios. The complexity of SD is mainly determined by the Initial Radius (IR) method and the number of visited nodes during detection. This paper proposes both an efficient IR and a new metric constraint in the tree searching algorithm, that significantly decrease the number of visited nodes and render SD feasible for large-scale systems. In addition, a hardware implementation featured with a one-node-per-cycle architecture, minimizes the latency of the detection process. Trade-offs between bit error rate (BER) performance and computational complexity are presented, either modifying the backtracking mechanism or limiting the number of radius updates. Simulation results prove that the proposed optimizations are effective for both correlated and uncorrelated channels, regardless the level of noise. The decoding gain of SD compared to the low-complexity Linear Detectors (LD) is higher in the presence of correlation than in the uncorrelated case. However, as expected, spatial correlation adversely affects the performance and the complexity of SD. Simulation results reported here also confirm that correlation at the side equipped with more antennas is less detrimental. Hardware aspects are examined for both a Virtex-7 FPGA device and a 28-nm ASIC technology.</div>

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BER Evaluation of Linear Detectors in Massive MIMO Systems Under Imperfect Channel Estimation Effects

Cited by 23 publications

References 38 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

Hardware Implementation and Performance Analysis of Improved Sphere Decoder in Spatially Correlated Massive MIMO Channels

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