Approximate iteration detection with iterative refinement in massive MIMO systems

Tang, Chuan; Liu, Cang; Ling, Yuan; Zhang, Xing

doi:10.1049/iet-com.2016.0826

Cited by 22 publications

(11 citation statements)

References 18 publications

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“…Detectors based on approximate matrix inversions [34], [115], [85], [20], [44], [47], [80], [79], [113], [27], [35], [22], [95], [111], [28], [24], [50], [77], [92], [30], [72], [11] • The optimal performance can be obtained.…”

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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Section: Overviewmentioning

confidence: 99%

Low Complexity Linear Detectors for Massive MIMO: A Comparative Study

et al. 2021

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“…However, it is overly sensitive to the acceleration and relaxation parameters. Iterative refinement methods such as detection and precoding based on iterative refinement [34], NI and DBNI with iterative refinement [35] and refinement Jacobi [36] have been used to achieve high precision for M-MIMO detection. The extra refinement steps increase the cost of each iteration, thereby resulting in significantly increased complexity.…”

Section: Prior Workmentioning

confidence: 99%

A Low-Complexity Data Detection Algorithm for Massive MIMO Systems

et al. 2019

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Achieving high spectral efficiency in realistic massive multiple-input multiple-output (M-MIMO) systems entail a significant increase in implementation complexity, especially with respect to data detection. Linear minimum mean-squared error (LMMSE) can achieve near-optimal performance but involves computationally expensive large-scale matrix inversions. This paper proposes a novel computationally efficient data detection algorithm based on the modified Richardson method. We first propose an antenna-dependent approach for the robust initialization of the Richardson method. It is shown that the proposed initializer outperforms the existing initialization schemes by a large margin. Then, the Chebyshev acceleration technique is proposed to overcome the sensitivity of the Richardson method to relaxation parameter while simultaneously enhancing its convergence rate. We demonstrate that the proposed algorithm mitigates multiuser interference and offers significant performance gains via the iterative cancellation of the bias term by prior estimation. Hence, each step of the iteration routine gives a new and better estimate of the solution. An asymptotic expression for the average convergence rate is also derived in this paper. The numerical results show that the proposed algorithm outperforms the existing methods and achieves near-LMMSE performance with a significantly reduced computational complexity.

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“…Convergence rate of Nuemann series method is very slow when the number of users is large and requires a greater number of iterations to attain required performance, which increases the complexity significantly. Hence, Newton iteration method [23][24][25] was introduced to estimate the matrix inversion approximately with reduced computational burden. The accuracy of approximation critically depends on the number of Newton iterations and when it is set large to achieve a satisfactory error-rate performance it may result in an increased computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Improved Gauss–Seidel detector for large‐scale MIMO systems

et al. 2022

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Large-scale multiple-input multiple-output (LS-MIMO) is one of the promising technologies beyond the 5G cellular system in which large antenna arrays at the base station (BS) improve the system capacity and energy-efficiency. However, the large number of antennas at the BS makes it challenging to design low-complexity high-performance data detectors. Thus, a number of iterative detection methods, such as Gauss-Seidel and conjugate gradient, are introduced to achieve complexity-performance tradeoff. However, their performance deteriorates for the systems with small BS-to-user antenna ratio or for the channels that exhibit correlation. This paper proposes a new efficient iterative detection algorithm based on the improved Gauss-Seidel iteration to address this problem. The proposed method performs one conjugate gradient iteration that enables better performance with less number of iterations. A new hybrid iteration is introduced and a low-complexity initial estimation is utilised to enhance detection accuracy while reducing the complexity further. In addition, a novel preconditioning technique is proposed to maintain the benefits of the proposed detector in correlated MIMO channels. It is mathematically demonstrate that the proposed detector achieves low approximated error. Theoretical analysis and numerical results show that the proposed algorithm provides a faster convergence rate compared to conventional methods.

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Approximate iteration detection with iterative refinement in massive MIMO systems

Cited by 22 publications

References 18 publications

Low Complexity Linear Detectors for Massive MIMO: A Comparative Study

Low Complexity Linear Detectors for Massive MIMO: A Comparative Study

A Low-Complexity Data Detection Algorithm for Massive MIMO Systems

Improved Gauss–Seidel detector for large‐scale MIMO systems

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