Optimization of number of base station antennas in downlink massive MIMO and analysis of imperfect channel state information by perfection factor

Ramisetty, Uma Maheswari; Chennupati, Sumanth Kumar

doi:10.1016/j.jestch.2019.10.009

Cited by 7 publications

(6 citation statements)

References 23 publications

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“…This paper's DT algorithm was implemented using the Nave Bayes classifier, the K-Nearest Neighbour algorithm, SVM, and the Bagging ensemble classifier. In order to discover the best answer, multiple researchers [10][11][12] worked on various optimization strategies. They haven't, however, taken machine learning into account.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Fraud Detection Prediction Using Synthetic Minority Over-Sampling Technique

Ramisetty¹,

Kumar²,

Mishra³

et al. 2023

Atlantis Highlights in Intelligent Systems

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Credit cards are increasingly being used in real life for a wide variety of purposes. Because of the growing number of users, the number of scammers is also growing at an accelerating rate. E-commerce fraud detection methods are critical for reducing losses. Models developed in the past using unbalanced datasets show a high degree of accuracy. The precision, recall, and weighted average precision and recall are all quite low for the models. As a result of this research, techniques such as logistic regression (LR) and random forest (RF), along with SMOTE, were developed to increase the model's performance with imbalanced datasets. SMOTE techniques are used to balance the datasets because they are so unbalanced. SMOTE analysis has revealed that the RF with SMOTE is the best model for detecting credit card fraud, with accuracy, precision, and recall scores of 99.95%, 85.40%, 86.02%, and 85.71%, respectively.

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Section: Introductionmentioning

confidence: 99%

Analysis of Fraud Detection Prediction Using Synthetic Minority Over-Sampling Technique

Ramisetty¹,

Kumar²,

Mishra³

et al. 2023

Atlantis Highlights in Intelligent Systems

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“…At the present, massive multiple-input multiple-output (MIMO) is a fundamental technology that can deliver consistently acceptable service to wireless terminals in areas with significant mobility by using more antennas at both transmission and reception points to capture fragmented waves [1]. A key technology for 5G is massive MIMO, which gives users more reliable connections, better data rates, and an overall improved user experience [2]. It is also highly expected to be involved in 6G [3].…”

Section: Introductionmentioning

confidence: 99%

Power Optimization of Massive MIMO Using Quantum Genetic Algorithm

Almasaoodi¹,

Sabaawi²,

Gaily³

et al. 2023

1st Workshop on Intelligent Infocommunication Networks, Systems and Services

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The massive Multiple-Input Multiple-Output (MIMO) is key enabling technology for the 5G and 6G cellular technologies, which allows dramatically improving the energy efficiency of the network, as well as increasing the transmission bit rate. In this paper, we developed a new quantum genetic algorithm for handling non-constrained objective functions. The study aims to compare the performance of the classical genetic algorithm and its newly extended quantum version in minimizing the overall transmit power of the downlink massive MIMO system. In terms of the total number of performed generations and total transmit power, a simulation environment was used to demonstrate the efficiency of the quantum genetic strategy versus the classical one.

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“…There is a paucity of work in the literature covering the analysis of M‐MIMO detectors under ICSI 34 . Little information is present on the BER analysis of detectors under ICSI with different numbers of BS antennas 35 . With PCSI and ICSI, the ZF detector is investigated for the uplink massive multi‐user scenario to analyze the spectrum efficiency and error performance; however, spatial modulation with one transmit antenna active at one time slot is employed over the composite Rayleigh fading channel, thus reducing the overall system capacity 36 .…”

Section: Introductionmentioning

confidence: 99%

Performance analysis and comparison of approximate detectors with linear detectors in massive MIMO systems under imperfect channel state information

Khurshid

Imran

Wakeel

2023

Trans Emerging Tel Tech

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SummaryMassive MIMO systems are a key technology for enhancing the capacity and reliability of wireless communication systems. However, to unlock the full potential of massive MIMO systems, accurate channel state information is crucial at the receivers. In practice, channel estimation errors are inevitable, leading to significant performance degradation in traditional massive MIMO equalizers that assume perfect channel state information. In this work, we investigate the performance of approximate massive MIMO detectors, specifically the Gauss‐Seidel and Neumann series, over a time‐invariant channel with imperfect channel state information. We derive the expressions of signal‐to‐interference‐plus‐noise ratio and average bit error rate for approximate massive MIMO detectors in a unicellular environment using M‐ary quadrature amplitude modulation. Additionally, we compare the performance of linear detectors, including zero‐forcing, minimum‐mean‐squared‐error, and maximal‐ratio combining, with that of the approximate detectors. Through extensive Monte Carlo numerical simulations and various system configurations, we demonstrate that the Gauss‐Seidel detector, under imperfect channel state information, achieves comparable bit error rate performance to the zero‐forcing and minimum‐mean‐squared‐error detectors while requiring less computational complexity. Our study suggests that the Gauss‐Seidel detector has the potential to be used in massive MIMO systems in the presence of imperfect channel state information, highlighting its practical relevance in real‐world scenarios.

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Optimization of number of base station antennas in downlink massive MIMO and analysis of imperfect channel state information by perfection factor

Cited by 7 publications

References 23 publications

Analysis of Fraud Detection Prediction Using Synthetic Minority Over-Sampling Technique

Analysis of Fraud Detection Prediction Using Synthetic Minority Over-Sampling Technique

Power Optimization of Massive MIMO Using Quantum Genetic Algorithm

Performance analysis and comparison of approximate detectors with linear detectors in massive MIMO systems under imperfect channel state information

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