Low-complexity MMSE detector based on refinement Jacobi method for massive MIMO uplink

Negrete, Juan Carlos; Flores, Andrea Carolina

doi:10.1016/j.phycom.2017.12.005

Cited by 23 publications

(14 citation statements)

References 15 publications

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“…In layman terms, the proposed iterative algorithm expression can be easily deduced from the combination of the equations (13) and (17) as follows:…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The low-complexity of Jacobi iterative method leads some authors to work on improving its performance in terms of convergence rate. The refinement of Jacobi iterative method proposed in [13] has offered a new low-complexity MMSE detector, in fact, this refinement has been given by the use of band matrix to accelerate the convergence rate and guarantee the nearoptimal BER performance. In [14], a Jacobi method-based scheme has been established by using an initial estimation provided by steepest descent technique.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Damped Jacobi Methods Based on Two Different Matrices for Signal Detection in Massive MIMO Uplink

Naceur

2021

J. Microw. Optoelectron. Electromagn. Appl.

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For massive multiple-input multiple-output (m-MIMO) uplink, the performances of the linear minimum mean-square error (MMSE) detector are considered near optimal, and they occupy benchmark place for most linear iterative detectors. However, the MMSE algorithm is known by its load computational complexity due to the implication of large-scale matrix inversions, and in other hand, iterative methods are often preferred in signal detection because of its low complexity. In this paper, we propose a New Damped Jacobi (NDJ) detector in order to improve the performance of the classical Jacobi linear algorithm. Starting from the classical Jacobi technique to our new proposal, we go through the development of two variants; one uses a damping factor and the other uses a stair-matrix. However, the NDJ incorporates a damping factor in its construction and basing also on stair matrix instead of diagonal matrix. The performances in terms of convergence and low complexity of each Jacobi variant studied in this paper are analyzed. Finally, some simulation examples are given to illustrate the advantages of the new proposed algorithm.

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“…In layman terms, the proposed iterative algorithm expression can be easily deduced from the combination of the equations (13) and (17) as follows:…”

Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Damped Jacobi Methods Based on Two Different Matrices for Signal Detection in Massive MIMO Uplink

Naceur

2021

J. Microw. Optoelectron. Electromagn. Appl.

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“…147 As well as indoor/outdoor communications technologies, for example, millimeter wave and visible light communication utilizing high frequencies/large antenna arrays, mobile and static small cells. 148 In addition, Wi-Fi overlay/offloads, massive MIMO (multiusers), 149,150 and multihop/meshed networks. 5…”

Section: Cognitive-5g Networkmentioning

confidence: 99%

A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach

Oyewobi

Djouani

Kurien

2020

Trans Emerging Tel Tech

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Integral and crucial to performance of wireless sensor networks (WSNs), and specifically industrial wireless sensor network (IWSN) is stable, robust, reliable, and ubiquitous communications system. Though, wired communications system is suitable for industrial communications and is resilient to shadowing and multipath fading effects of industrial‐WSN environments, yet its wireless counterpart is a much preferred industrial communications technology due to reduced cost and high flexibility which it offers in comparison to wired communications. However, overcrowding of the industrial, scientific, and medical band, where IWSN is deployed together with other heterogeneous technologies, as well as resultant scarcity of usable frequency spectrum has restrained exclusive application of wireless technology for industrial communications. Nonetheless, cognitive radio (CR) has ability to increase spectrum utilization efficiency and channel capacity for industrial wireless communications (IWC) through opportunistic/dynamic spectrum access (DSA) technique. In this review article, we examine how DSA can benefit IWC through exploitation of new perspectives of white space definitions in the licensed bands as well as unlicensed bands. While discussing the potential of DSA for IWC, we have considered the unique characteristics of IWC as well as technical challenges and issues imposed by industrial‐WSN. Accordingly, we have suggested and proffered appropriate CR‐based solutions in mitigating some of the challenges where necessary.

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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%

“…Moreover, its complexity is even higher than the exact matrix inversion algorithm for more than three expansion terms. To reduce the computational complexity by replacing the NSE, several other low-complexity approximate data detection methods [22]- [36] based on classical iterative methods have been proposed. However, all these methods exhibit degraded performance when the number of users increases and require a larger number of iterations to achieve better performance with even higher complexity than the exact matrix inversion (see the analysis in Section-III).…”

Section: Introductionmentioning

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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Low-complexity MMSE detector based on refinement Jacobi method for massive MIMO uplink

Cited by 23 publications

References 15 publications

Damped Jacobi Methods Based on Two Different Matrices for Signal Detection in Massive MIMO Uplink

Damped Jacobi Methods Based on Two Different Matrices for Signal Detection in Massive MIMO Uplink

A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach

A Low-Complexity Data Detection Algorithm for Massive MIMO Systems

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