Spectral efficiency analysis of multi‐cell multi‐user massive MIMO over channel aging

Xin, Yuanxue; Shi, Pengfei; Xia, Xinjiang; Fan, Xinnan

doi:10.1049/iet-com.2019.1079

Cited by 3 publications

(4 citation statements)

References 22 publications

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“…However, there is no consideration of study on the efect of pilot contamination on spectral efciency and the study with multicell massive MIMO was not considered in their methods. In [14], using Rician fading channel, the spectral efciency analysis was also studied for the multicell massive MIMO system that is similar to references [19][20][21][22][23]. But, the only diference is that the results analysis is based on the linear maximal-ratio combining detector and Rician fading channel for diferent values of Rician factor and BS antennas.…”

Section: Related Workmentioning

confidence: 98%

Spectral Efficiency Analysis for Uplink Multicell Massive MIMO Cellular Communication System under Fading Channels

Abebaw,

Shakya,

Gelmecha

et al. 2023

Journal of Electrical and Computer Engineering

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In multicell massive MIMO system, the maximum limit on area throughput can be achieved by improving spectral efficiency and cell density, as well as bandwidth. In order to evaluate the area throughput for such scenarios, the spectral efficiency (SE) that utilizes the linear zero forcing uplink combining scheme, can be modeled under the Rician fading channel and the BS in case of up-links, is responsible to estimate the channel. Different from existing work, the proposed model incorporates various estimators such as minimum mean square error (MMSE), element-wise minimum mean square error estimators under Rician fading. The multicell scenarios with uplink (UL) massive MIMO has been analyzed using the proposed model under different cases such as pilot reuse factor, coherence block length, different number of antennas, and different estimators. The simulation results and analysis are presented based on these parameters. It is found that the average summation of SE per cell can be improved by optimizing MMSE channel estimation using ZF UL combiner, installing multiple BS antennas, serving multiple number of UEs per cell, and using efficient pilot reuse factor. The MMSE and ZF uplink combining are found to be more suitable in improving SE as compared to MMSE-MR. For example, the uplink SE of MMSE channel estimator for pilot reuse factors, 1, 3, and 4, is calculated as 22.5 bit/s/Hz/cell, 22.3 bit/s/Hz/cell, and 21 bit/s/Hz/cell, respectively. The uplink SE for EW-MMSE channel estimator with pilot reuse factors, 1, 3, and 4, is calculated as 22.5 bit/s/Hz/cell, 22 bit/s/Hz/cell, and 22 bit/s/Hz/cell, respectively. For the uplink SE of LS channel estimators, it can be 17.9 bit/s/Hz/cell, 20.2 bit/s/Hz/cell, and 20 bit/s/Hz/cell with pilot reuse factors as f = 1, 3, and 4, respectively. So, for f = 3, the maximum calculated uplink SE for MMSE, EW-MMSE, and LS is 17.6 bit/s/Hz/cell, 17.8 bit/s/Hz/cell, and 13 bit/s/Hz/cell, respectively. It can be concluded that the improved performance is obtained by reducing the pilot contamination at a pilot reuse factor f = 3 with different values of SNR, coherence block length, number of UEs, and number of BS antennas. There is also trade-off between the pilot contamination mitigation and the larger SE. However, there is not much effect on coherence block as when it increases, then the SE increases as well.

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Section: Related Workmentioning

confidence: 98%

Spectral Efficiency Analysis for Uplink Multicell Massive MIMO Cellular Communication System under Fading Channels

Abebaw,

Shakya,

Gelmecha

et al. 2023

Journal of Electrical and Computer Engineering

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“…The EE is tightly connected to the value of spectral efficiency (SE). The SE can be described as the average number of information bits that can be successfully transmitted through communication channels [8,9]. One writes the expression of SE as,…”

Section: Related Workmentioning

confidence: 99%

“…First, the algorithm takes as an initial input, the maximum number of transmit antennas M at the BS, that is, this initial information is necessary for determining the size of the regions of the quantum initialisation stage for the QGA. Next, we set the stochastic parameters of the QEVSA regarding to the formula (9). More details on how to set up the stochastic variables of the QEVSA are described in [17,45].…”

Section: Quantum Parents Generation Stagementioning

confidence: 99%

\text{SE}=\frac{\text{Total}\,\text{bit}\,\text{rate}}{\text{Channel}\,\text{bandwidth}}

…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy efficiency optimisation in massive multiple‐input, multiple‐output network for 5G applications using new quantum genetic algorithm

Sabaawi,

Almasaoodi,

El Gaily

et al. 2023

IET Networks

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Devising efficient optimisation methods has been a subject of great research attention since current evolving trends in communication networks, machine learning, and other cutting‐edge systems that need a fast and accurate optimised computational model. Classical computers became incapable of handling new optimisation problems posed by newly emerging trends. Quantum optimisation algorithms appear as alternative solutions. The existing bottleneck that restricts the use of the newly developed quantum strategies is the limited qubit size of the available quantum computers (the size of the most recent universal quantum computer is 433 qubits). A new quantum genetic algorithm (QGA) is proposed that handles the presented problem. A quantum extreme value searching algorithm and quantum blind computing framework are utilised to extend the search capabilities of the GA. The quantum genetic strategy is exploited to maximise energy efficiency at full spectral efficiency of massive multiple‐input, multiple‐output (M‐MIMO) technology as a toy example for pointing out the efficiency of the presented quantum strategy. The authors run extensive simulations and prove how the presented quantum method outperforms the existing classical genetic algorithm.

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BER of Massive MIMO in time-variant channels using multiplexed, superimposed and hybrid channel estimation techniques

Altamirano

Mora

Almeida

2021

AEU - International Journal of Electronics and Communications

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Spectral efficiency analysis of multi‐cell multi‐user massive MIMO over channel aging

Cited by 3 publications

References 22 publications

Spectral Efficiency Analysis for Uplink Multicell Massive MIMO Cellular Communication System under Fading Channels

Spectral Efficiency Analysis for Uplink Multicell Massive MIMO Cellular Communication System under Fading Channels

Energy efficiency optimisation in massive multiple‐input, multiple‐output network for 5G applications using new quantum genetic algorithm

BER of Massive MIMO in time-variant channels using multiplexed, superimposed and hybrid channel estimation techniques

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