Low Complexity Linear Detectors for Massive MIMO: A Comparative Study

Albreem, Mahmoud A.; Salah, Wael A.; Кумар, Арун; Alsharif, Mohammed H.; Rambe, Ali Hanafiah; Jusoh, M.; Uwaechia, Anthony Ngozichukwuka

doi:10.1109/access.2021.3065923

Cited by 40 publications

(17 citation statements)

References 101 publications

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“…FIGURE5 Performance comparison of the proposed BFGS scheme with the existing linear detection algorithms under 128 × 32 antenna configurations with 64-QAM modulation in the coded system…”

mentioning

confidence: 99%

Low‐complexity linear massive MIMO detection based on the improved BFGS method

2022

IET Communications

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Linear minimum mean square error (MMSE) detection achieves a good trade‐off between performance and complexity for massive multiple‐input multiple‐output (MIMO) systems. To avoid the high‐dimensional matrix inversion involved, MMSE detection can be transformed into an unconstrained optimization problem and then solved by efficient numerical algorithms in an iterative way. Three low‐complexity Broyden‐Fletcher‐Goldfarb‐Shanno (BFGS) quasi‐Newton methods are proposed to iteratively realize massive MIMO MMSE detection without matrix inversion. The complexity can be reduced from scriptOfalse(K3false)$\mathcal {O}(K^{3})$ to scriptOfalse(LK2false)$\mathcal {O}(LK^{2})$, where K and L denote the number of users and iterations, respectively. Leveraging the special properties of massive MIMO, the authors first explore a simplified BFGS method (named S‐BFGS) to alleviate the computational burden in the search direction. For lower complexity, BFGS method with the unit step size (named U‐BFGS) is presented subsequently. When the base station (BS)‐to‐user‐antenna ratio (BUAR) is large enough, the two proposed BFGS methods can be integrated (named U‐S‐BFGS) to further reduce complexity. In addition, an efficient initialization strategy is devised to accelerate convergence. Simulation results verify that the proposed detection scheme can achieve near‐MMSE performance with a small number of iterations L as low as 2 or 3.

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“…FIGURE5 Performance comparison of the proposed BFGS scheme with the existing linear detection algorithms under 128 × 32 antenna configurations with 64-QAM modulation in the coded system…”

mentioning

confidence: 99%

Low‐complexity linear massive MIMO detection based on the improved BFGS method

2022

IET Communications

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“…Then the problem we solve is Then solve a set of linear equations by calculating the solution of the iterative be havior [37].…”

Section: Gauss-seidel Iterative Methodsmentioning

confidence: 99%

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

Yao

Song

et al. 2022

EURASIP J. Adv. Signal Process.

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In massive multiple-input multiple-output (MIMO) systems with single- antenna user equipment (SAUE) or multiple-antenna user equipment (MAUE), with the increase of the number of received antennas at base station, the complexity of traditional detectors is also increasing. In order to reduce the high complexity of parallel running of the traditional Gauss-Seidel iterative method, this paper proposes a model-driven deep learning 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. In order to improve the symbol error ratio (SER) of BGS-Net under MAUE system, we propose Improved BGS-Net. The simulation results show that, compared with the existing model-driven algorithms, BGS-Net has lower complexity and similar the detection performance; good robustness, and its performance is less affected by changes in the number of antennas; Improved BGS-Net can improve the detection performance of BGS-Net.

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“…In the CG method, the solution depends on scalar parameters. It is also refined iteratively where the search is performed in the conjugate direction with a movement towards the best solution [32], [108].In [109], a detector based on the CG algorithm is implemented in Xilinx Virtex-7 FPGA for a 128 × 8. It is also implemented using a GPU platform [110].…”

Section: Overview Of Detection Schemes In Massive Mimomentioning

confidence: 99%

Deep Learning for Massive MIMO Uplink Detectors

Albreem

Alhabbash²,

Shahabuddin

et al. 2022

IEEE Commun. Surv. Tutorials

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Detection techniques for massive multiple-input multiple-output (MIMO) have gained a lot of attention in both academia and industry. Detection techniques have a significant impact on the massive MIMO receivers' performance and complexity. Although a plethora of research is conducted using the classical detection theory and techniques, the performance is deteriorated when the ratio between the numbers of antennas and users is relatively small. In addition, most of classical detection techniques are suffering from severe performance loss and/or high computational complexity in real channel scenarios. Therefore, there is a significant room for fundamental research contributions in data detection based on the deep learning (DL) approach. DL architectures can be exploited to provide optimal performance with similar complexity of conventional detection techniques. This paper aims to provide insights on DL based detectors to a generalist of wireless communications. We garner the DL based massive MIMO detectors and classify them so that a reader can find the differences between various architectures with a wider range of potential solutions and variations. In this paper, we discuss the performance-complexity profile, pros and cons, and implementation stiffness of each DL based detector's architecture. Detection in cell-free massive MIMO is also presented. Challenges and our perspectives for future research directions are also discussed. This article is not meant to be a survey of a mature-subject, but rather serve as a catalyst to encourage more DL research in massive MIMO.

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Low Complexity Linear Detectors for Massive MIMO: A Comparative Study

Cited by 40 publications

References 101 publications

Low‐complexity linear massive MIMO detection based on the improved BFGS method

Low‐complexity linear massive MIMO detection based on the improved BFGS method

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

Deep Learning for Massive MIMO Uplink Detectors

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