Multi‐cell MMSE data detection for massive MIMO: new simplified bounds

Alwakeel, Mohammed M.; Mehana, Ahmed Hesham

doi:10.1049/iet-com.2018.5029

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…In an MMSE detector, the mean square error (MSE) between

and

is minimized as

The MMSE detector takes into consideration the impact of noise as

where I is the identity matrix. In MMSE detector, the signal is estimated as

The MMSE in ( 8 ) relies on a reduction of the noise enhancement and needs an awareness of the SNR [ 48 ]. Thus, the MMSE outperforms the ZF- and MF-based detectors.…”

Section: Overviewmentioning

confidence: 99%

A Low Complexity Near-Optimal Iterative Linear Detector for Massive MIMO in Realistic Radio Channels of 5G Communication Systems

Albreem

Alsharif

Kim

2020

Entropy

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Massive multiple-input multiple-output (M-MIMO) is a substantial pillar in fifth generation (5G) mobile communication systems. Although the maximum likelihood (ML) detector attains the optimum performance, it has an exponential complexity. Linear detectors are one of the substitutions and they are comparatively simple to implement. Unfortunately, they sustain a considerable performance loss in high loaded systems. They also include a matrix inversion which is not hardware-friendly. In addition, if the channel matrix is singular or nearly singular, the system will be classified as an ill-conditioned and hence, the signal cannot be equalized. To defeat the inherent noise enhancement, iterative matrix inversion methods are used in the detectors’ design where approximate matrix inversion is replacing the exact computation. In this paper, we study a linear detector based on iterative matrix inversion methods in realistic radio channels called QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa) package. Numerical results illustrate that the conjugate-gradient (CG) method is numerically robust and obtains the best performance with lowest number of multiplications. In the QuaDRiGA environment, iterative methods crave large n to obtain a pleasurable performance. This paper also shows that when the ratio between the user antennas and base station (BS) antennas ( β ) is close to 1, iterative matrix inversion methods are not attaining a good detector’s performance.

show abstract

“…In an MMSE detector, the mean square error (MSE) between

and

is minimized as

The MMSE detector takes into consideration the impact of noise as

where I is the identity matrix. In MMSE detector, the signal is estimated as

The MMSE in ( 8 ) relies on a reduction of the noise enhancement and needs an awareness of the SNR [ 48 ]. Thus, the MMSE outperforms the ZF- and MF-based detectors.…”

Section: Overviewmentioning

confidence: 99%

A Low Complexity Near-Optimal Iterative Linear Detector for Massive MIMO in Realistic Radio Channels of 5G Communication Systems

Albreem

Alsharif

Kim

2020

Entropy

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show abstract

“…For example, increasing the performance of the practical signal detection algorithm in the uplink to accommodate multiuser interferences. Growth in the number of transmit antennas has been shown to cause a fast increase in the complexity of ideal maximum likelihood (ML) detectors [6]. As a result, it becomes impracticable for large-scale MIMO systems, and their relevance diminishes as a result of this.…”

Section: Introductionmentioning

confidence: 99%

Data detection method for uplink massive MIMO systems based on the long recurrence enlarged conjugate gradient

Jawarneh

Albataineh

Kadoch

2022

IJECE

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<p>Although the mean square error (MMSE) approach is recognized to be near optimal for uplinking large-scale multiple-input-multiple-output (MIMO) systems, there are certain difficulties in the procedure related to matrix inversion. The long recurrence enlarged conjugate gradient (LRE-CG) approach is proposed in this study as a way to iteratively realize the MMMS algorithm while avoiding the complications of matrix inversion. In addition, a diagonal-approximate starting solution to the LRE-CG approach was used to speed up the conversion rate and reduce the complications required. It has been discovered that the LRE-CG-based approach has the ability to significantly reduce computational complexity. By comparing simulation results, it is clear that this new methodology surpasses well-established wayslike the Neumann series approximation-based method and the Gauss-Siedel iterative method. With a small number of iterations, the suggested approach achieves near-optimal performance of a standard MMSE algorithm.</p>

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“…Massive MIMO was combined with general orthogonal precoding for high mobility scenarios in [7]. A minimum mean square error (MMSE) detector was used for channel estimation of massive MIMO systems [8]. In [9], the pairwise error probability (PEP) of the massive multiuser MMSE-MIMO systems was derived.…”

Section: Introductionmentioning

confidence: 99%

Outage Probability Performance Prediction for Mobile Cooperative Communication Networks Based on Artificial Neural Network

Wang

2019

Sensors

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This paper investigates outage probability (OP) performance predictions using transmit antenna selection (TAS) and derives exact closed-form OP expressions for a TAS scheme. It uses Monte-Carlo simulations to evaluate OP performance and verify the analysis. A back-propagation (BP) neural network-based OP performance prediction algorithm is proposed and compared with extreme learning machine (ELM), locally weighted linear regression (LWLR), support vector machine (SVM), and BP neural network methods. The proposed method was found to have higher OP performance prediction results than the other prediction methods.

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Multi‐cell MMSE data detection for massive MIMO: new simplified bounds

Cited by 5 publications

References 30 publications

A Low Complexity Near-Optimal Iterative Linear Detector for Massive MIMO in Realistic Radio Channels of 5G Communication Systems

A Low Complexity Near-Optimal Iterative Linear Detector for Massive MIMO in Realistic Radio Channels of 5G Communication Systems

Data detection method for uplink massive MIMO systems based on the long recurrence enlarged conjugate gradient

Outage Probability Performance Prediction for Mobile Cooperative Communication Networks Based on Artificial Neural Network

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