Matrix inversion-less signal detection using SOR method for uplink large-scale MIMO systems

Gao, Xinyu; Dai, Linglong; Hu, Yuting; Wang, Zhongxu; Hanzo, Lajos

doi:10.1109/glocom.2014.7037314

Cited by 81 publications

(61 citation statements)

References 13 publications

(36 reference statements)

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“…Gao et al [102] Presented an algorithm based on successive overrelaxation (SOR) where the results showed an almost similar to the MMSE method in achieving low complexity signal detection.…”

Section: Gao Et Al [101]mentioning

confidence: 99%

The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review

et al. 2019

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Network latency will be a critical performance metric for the Fifth Generation (5G) networks expected to be fully rolled out in 2020 through the IMT-2020 project. The multi-user multiple-input multiple-output (MU-MIMO) technology is a key enabler for the 5G massive connectivity criterion, especially from the massive densification perspective. Naturally, it appears that 5G MU-MIMO will face a daunting task to achieve an end-to-end 1 ms ultra-low latency budget if traditional network set-ups criteria are strictly adhered to. Moreover, 5G latency will have added dimensions of scalability and flexibility compared to prior existing deployed technologies. The scalability dimension caters for meeting rapid demand as new applications evolve. While flexibility complements the scalability dimension by investigating novel non-stacked protocol architecture. The goal of this review paper is to deploy ultra-low latency reduction framework for 5G communications considering flexibility and scalability. The Four (4) C framework consisting of cost, complexity, cross-layer and computing is hereby analyzed and discussed. The Four (4) C framework discusses several emerging new technologies of software defined network (SDN), network function virtualization (NFV) and fog networking. This review paper will contribute significantly towards the future implementation of flexible and high capacity ultra-low latency 5G communications.

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“…Gao et al [102] Presented an algorithm based on successive overrelaxation (SOR) where the results showed an almost similar to the MMSE method in achieving low complexity signal detection.…”

Section: Gao Et Al [101]mentioning

confidence: 99%

The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review

et al. 2019

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“…7(b). For each clock cycle (T01 to T32), the four parallel MU (MU1 to MU4) outputs are the 64 (= outputs (12) S (i) (13) S (i) (14) S (i) (15) S (i) (16) h 1,1 ·Y 1 of 4 MUs × 16 rows) product term numbers (h m,n ·Y m ) presented in Fig. 7(c).…”

Section: H   H Y Hsmentioning

confidence: 99%

“…Using only the first two terms of the Neumann series, the computational complexity is reduced from O(U 3 ) to O(U 2 ) [10]. However, the BER performance of the Neumann series approximation with two terms is considerably less than that of the exact matrix inversion [11], [13], [14]. For low-complexity and near-optimal signal detection of massive MIMO systems, stationary iterative methods have been proposed [11]- [17].…”

Section: Approximate Matrix Inversion and Iterative Signal Detection mentioning

confidence: 99%

“…In stationary iterative methods, the U-dimensional transmitted symbol vector is approximated to a solution of the ith iteration. The iterative solution of the Richardson [11], [12], successive over-relation (SOR) [13], [14], and Jacobi-based [15] iterative methods are presented in the following:…”

Section: Approximate Matrix Inversion and Iterative Signal Detection mentioning

confidence: 99%

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Low-Complexity Massive MIMO Detectors Based on Richardson Method

Kang¹,

Yoon²

2017

ETRI Journal

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In the uplink transmission of massive (or large-scale) multi-input multi-output (MIMO) systems, large dimensional signal detection and its hardware design are challenging issues owing to the high computational complexity. In this paper, we propose low-complexity hardware architectures of Richardson iterative methodbased massive MIMO detectors. We present two types of massive MIMO detectors, directly mapped (type1) and reformulated (type2) Richardson iterative methods. In the proposed Richardson method (type2), the matrixby-matrix multiplications are reformulated to matrixvector multiplications, thus reducing the computational complexity from O(U 2 ) to O(U). Both massive MIMO detectors are implemented using a 65 nm CMOS process and compared in terms of detection performance under different channel conditions (high-mobility and flat fading channels). The hardware implementation results confirm that the proposed type1 Richardson method-based detector demonstrates up to 50% power savings over the proposed type2 detector under a flat fading channel. The type2 detector indicates a 37% power savings compared to the type1 under a high-mobility channel.

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“…Many massive Multiple Input Multiple Output (MIMO) equalization schemes consider only perfect channel estimation [6], [7] even without channel coding. We think the propagation of the channel estimation error inside the Multi User -Multiple Input Multiple Output (MU-MIMO) equalization is not straight forward.…”

Section: Introductionmentioning

confidence: 99%

Robust massive MIMO equilization for mmWave systems with low resolution ADCs

Roth

Nossek

2018

2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)

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Leveraging the available millimeter wave spectrum will be important for 5G. In this work, we investigate the performance of digital beamforming with low resolution ADCs based on link level simulations including channel estimation, MIMO equalization and channel decoding. We consider the recently agreed 3GPP NR type 1 OFDM reference signals. The comparison shows sequential DCD outperforms MMSE-based MIMO equalization both in terms of detection performance and complexity. We also show that the DCD based algorithm is more robust to channel estimation errors. In contrast to the common believe we also show that the complexity of MMSE equalization for a massive MIMO system is not dominated by the matrix inversion but by the computation of the Gram matrix.

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Matrix inversion-less signal detection using SOR method for uplink large-scale MIMO systems

Cited by 81 publications

References 13 publications

The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review

The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review

Low-Complexity Massive MIMO Detectors Based on Richardson Method

Robust massive MIMO equilization for mmWave systems with low resolution ADCs

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