Spectral Efficiency of Massive MIMO Communication Systems with Zero Forcing and Maximum Ratio Beamforming

Ali, Asif; Qureshi, Imran Ali; Memon, Abdul Latif; Memon, Sufyan Ali; Saba, Erum

doi:10.14569/ijacsa.2018.091254

Cited by 4 publications

(4 citation statements)

References 23 publications

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“…Te simulation parameters used for analysis are listed in Table 1. Increasing the number of orthogonal pilot sequence is a straight forward way to decrease pilot contamination but it reduces the data rate; another mechanism is required to negotiate the pilot contamination problem and required data [13,15,[18][19][20]. Pilot reuse factor is a mechanism to improve the required throughput by reducing the pilot contamination.…”

Section: Resultsmentioning

confidence: 99%

“…Results claimed that the improved area throughput can be achieved with the M-MMSE technique, including the channel models such as the Laplacian-centralized scattering spatially correlated Rayleigh fading, one-ring scattering correlated Rayleigh fading, and uncorrelated Rayleigh fading [9]. In [13,15], the pilot-based channel estimators were used to generate the channel state information of the desired system to optimize the pilot assignment. Te authors had identifed the pilot contamination using the pilot-assisted estimation, but they eliminated the channel estimated.…”

Section: Related Workmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…𝑪𝑷 𝟎 = 𝑷 𝑭𝑰𝑿 + 𝑴𝑷 𝑩𝑺 + 𝑵𝑷 𝑼𝑬 (5) Where, 𝑷 𝑼𝑬 denotes the consumed power by CPs of user equipment's single antenna.…”

Section: Tradeoff Between Energy Efficiency and Spectral Efficiency (...mentioning

confidence: 99%

“…1. Massive MIMO uplink and downlink overview [5] As mentioned in [5], such future technology may achieve higher spectral efficiency in UL and DL without needing more expensive and sophisticated Base Stations.…”

Section: Introductionmentioning

confidence: 99%

Throughput, Spectral, and Energy Efficiency of 5G Massive MIMO Applications Using Different Linear Precoding Schemes

Salah,

Rahouma,

Hussein

et al. 2022

International Journal of Electronics and Telecommunications

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On fifth-generation wireless networks, a potential massive MIMO system is used to meet the ever-increasing request for high-traffic data rates, high-resolution streaming media, and cognitive communication. In order to boost the trade-off between energy efficiency (EE), spectral efficiency (SE), and throughput in wireless 5G networks, massive MIMO systems are essential. This paper proposes a strategy for EE 5G optimization utilizing massive MIMO technology. The massive MIMO system architecture would enhance the trade-off between throughput and EE at the optimum number of working antennas. Moreover, the EE-SE tradeoff is adjusted for downlink and uplink massive MIMO systems employing linear precoding techniques such as Multiple -Minimum Mean Square Error (M-MMSE), Regularized Zero Forcing (RZF), Zero Forcing (ZF), and Maximum Ratio (MR). Throughput is increased by adding more antennas at the optimum EE, according to the analysis of simulation findings. Next, utilizing M MMSE instead of RZF and ZF, the suggested trading strategy is enhanced and optimized. The results indicate that M-MMSE provides the best tradeoff between EE and throughput at the determined optimal ratio between active antennas and active users equipment's (UE).

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Issues and Challenges in the Implementation of 5G Technology

Sah

Bindle

Gulati

2021

Computer Networks and Inventive Communication Technologies

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Spectral Efficiency of Massive MIMO Communication Systems with Zero Forcing and Maximum Ratio Beamforming

Cited by 4 publications

References 23 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

Throughput, Spectral, and Energy Efficiency of 5G Massive MIMO Applications Using Different Linear Precoding Schemes

Issues and Challenges in the Implementation of 5G Technology

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