Low‐complexity signal detection for large‐scale MIMO systems with second‐order Richardson method

Khoso, Imran A.; Zhang, Xiaofei; Shaikh, Abdul Hayee

doi:10.1049/el.2020.0208

Cited by 10 publications

(7 citation statements)

References 9 publications

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“…Third, the conventional Richardson method utilized constant acceleration parameter. In addition, RI is suitable for a particular antenna configuration 26 whereas in practical systems antenna scenario differs. The goal of this work is to develop an algorithm that efficiently tackles these problems while accelerating the convergence rate simultaneously.…”

Section: Proposed Detector For Massive Mimo Systemsmentioning

confidence: 99%

“…Nonetheless, NSE only achieves reduction in complexity marginally and it is effective only for a large BS to UTs antenna ratio (eg,

\ge 16

). In References 25‐27, Richardson iteration (RI) has been introduced to achieve the performance of MMSE with reduced complexity. The existing RI based detectors 25,27 are overly sensitive to the relaxation parameter 28 .…”

Section: Introductionmentioning

confidence: 99%

“…In order to mitigate the complexity bottleneck of signal detection in massive MIMO systems, abundant works are devoted in recent years [24][25][26][27][28][29][30][31][32][33][34] to achieve MMSE performance with significantly lower complexity. Neumann series expansion (NSE) 24 based method is used to reduce the computing load by transforming the matrix inversion operation into a series of matrix-vector multiplications.…”

mentioning

confidence: 99%

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Joint steepest descent and non‐stationary Richardson method for low‐complexity detection in massive MIMO systems

Khoso

Zhang

Dai

et al. 2022

Trans Emerging Tel Tech

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Signal detection is a major challenge in massive multiple‐input multiple‐output (MIMO) wireless systems due to array of hundreds of antennas. Linear minimum mean square error (MMSE) enables near‐optimal detection performance in massive MIMO but suffers from unbearable computational complexity due to complicated matrix inversions. To address this problem, we propose a novel low‐complexity signal detector based on joint steepest descent (SD) and non‐stationary Richardson (NSR) iteration method. The SD is applied to get an efficient searching direction for the following NSR method to enhance the performance. The key idea of the proposed algorithm is to utilize a combination of the scaled‐diagonal initialization and the system‐ and iteration‐dependent acceleration mechanism, so that the convergence can be significantly speeded up. An antenna‐ and eigenvalue‐based scaling parameter is introduced for the proposed detector to further improve the error‐rate performance. We also provide convergence guarantees for the proposed technique. Numerical results demonstrate that the proposed joint detection approach attains superior performance compared to existing iterative approaches and provides a lower computational complexity than the conventional MMSE detector.

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Section: Proposed Detector For Massive Mimo Systemsmentioning

confidence: 99%

“…Nonetheless, NSE only achieves reduction in complexity marginally and it is effective only for a large BS to UTs antenna ratio (eg,

\ge 16

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Joint steepest descent and non‐stationary Richardson method for low‐complexity detection in massive MIMO systems

Khoso

Zhang

Dai

et al. 2022

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“…The accuracy and the number of computations are highly affected by the value of ω. In [46], a CG method is exploited to enhance the performance of second-order RI method. Moreover, ω is selected based on eigenvalues to speed up the convergence rate.…”

Section: F Richardsonmentioning

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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“…In addition, it has a large scope for parallel processing. Several iterative methods that estimate the transmitted multi-user data symbols directly by solving linear equation such as Jacobi method [28,29], Richardson iteration [30,31], Gauss-Seidal (GS) [32][33][34], and preconditioned GS (P-GS) [35] have been developed in the literature to overcome the complexity bottleneck of data detection in LS-MIMO systems. The GS based algorithms exhibit acceptable performance in different antenna scenarios and promise a faster convergence rate compared to Jacobi and Richardson iteration methods.…”

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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Low‐complexity signal detection for large‐scale MIMO systems with second‐order Richardson method

Cited by 10 publications

References 9 publications

Joint steepest descent and non‐stationary Richardson method for low‐complexity detection in massive MIMO systems

Joint steepest descent and non‐stationary Richardson method for low‐complexity detection in massive MIMO systems

Low Complexity Linear Detectors for Massive MIMO: A Comparative Study

Improved Gauss–Seidel detector for large‐scale MIMO systems

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