Design of Training Sequences for Multi User—MIMO with Accurate Channel Estimation Considering Channel Reliability Under Perfect Channel State Information Using Cuckoo Optimization

Ramisetty, Uma Maheswari; Chennupati, Sumanth Kumar; Kumar, G. V. Nagesh

doi:10.1007/s42835-021-00778-6

Cited by 4 publications

(2 citation statements)

References 37 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

Self Cite

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show abstract

“…According to study [18], utilizing a neural network can help enhance channel estimation performance. CNN [19] is described as a class of low-complexity channel estimators and is modeled by the MMSE channel estimator. The use of CNN and LSTM in fast time-changing channel estimation is demonstrated in study [20].…”

Section: Introductionmentioning

confidence: 99%

Enhancing 5G Massive MIMO Systems Using a Compressive Sensing-Based Approach

Kanaparthi,

Yarrabothu,

Sundar

2023

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The capacity of massive multiple-input multiple-output (MIMO) systems is significantly enhanced through the use of abundant antennas at the base station (BS), supporting a multitude of users. However, in Time Division Duplex (TDD) mode, the multiplexing of pilots inevitably leads to Pilot Contamination (PC). In this study, a novel approach to interference alignment and pilot purification in a large MIMO system was proposed, utilizing the principles of compressive sensing and deep learning. The primary aim of the Compressive Sensing-Based Location Scheduling System (CSLSS) is to mitigate PC issues while optimizing the pilot sequence for the user. By introducing an additional sequence, the pilot set is enhanced using our location-based decontamination technique. Furthermore, a Feed-Forward Convolutional Neural Network (FFCNN) is employed for interference alignment in the MIMO system. Experimental results indicate significant improvements in the parameters under consideration. The sum rate showed an increase of 74.6%, the Bit Error Rate (BER) improved by 57.8%, the Signal-to-Interference-plus-Noise Ratio (SINR) rose by 79.8%, and the spectral efficiency of the channel improved by 98% in terms of interference alignment. In addition, a compressive sensing-based parameter showed an enhancement of 98%. The Mean Square Error (MSE) for CSLSS was reduced by 64.5% for the Signal-to-Noise Ratio (SNR), and the BER for the proposed compressive sensing method improved by 41.2% for SNR. The throughput also improved, with an increase of 97.5% for SNR and 97.8% for the number of antennas used in the BS.

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Secure cluster head selection in wireless sensor networks using fuzzy based generic algorithm

Dhipa,

Sampath,

Seetha

et al. 2024

Wireless Netw

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Design of Training Sequences for Multi User—MIMO with Accurate Channel Estimation Considering Channel Reliability Under Perfect Channel State Information Using Cuckoo Optimization

Cited by 4 publications

References 37 publications

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

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

Enhancing 5G Massive MIMO Systems Using a Compressive Sensing-Based Approach

Secure cluster head selection in wireless sensor networks using fuzzy based generic algorithm

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