Signal Detection Based on Parallel Group Detection Algorithm for High-Load Massive MIMO Systems

Nguyen, Thanh-Binh; Le, Minh-Tuan; Ngo, Vu-Duc

doi:10.1155/2019/5609740

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…The authors then use SVM, random forest, DT, and the XGBOOST ML classifier to train the model on the transformed images. Nguyen et al [47] proposed two new detectors, called ZF-based PGD (ZF-PGD) and QRD-based PGD (QRD-PGD). PGD is further combined with the sorted longest base algorithm (SLB) to make the signal recovery more accurate, resulting in two new detectors, namely ZF-PGD-SLB and QRD-PGD-SLB.…”

Section: Related Workmentioning

confidence: 99%

Gradient-Boosted Convolutional Neural Network Malware Detection Technique for Android Smartphones

Chimeleze

Jamil

Teh

et al. 2022

Preprint

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Malware is malicious software designed to damage or infiltrate a computer system without the user's knowledge. Malware detection includes the process of detecting the presence of malware on the host system or determining whether the type of program is malicious or benign. Recently, machine learning (ML) algorithms have been used to detect malware effectively. Unfortunately, the core techniques require extensive feature learning, engineering and representation, which increases the computational time, error rate ratio and improves recall. The feature engineering phase of these methods can be alleviated by using more advanced ML approaches during the detection phase. In this article, we propose a gradient-boosted convolutional neural network (GB-CNN) to detect malware in Android smartphones. This proposed technique uses entropy-based feature selection technique to select relevant Android features and APKs. These selected features are fed to deep learning for classification. The classification results are then optimized by gradient boost machine learning. Comparative results show that GB-CNN outperforms other existing deep learning –based detection techniques, and is especially suitable for malware detection on Android devices, with improvements in terms of accuracy (3%), precision (1%) ,F-measure (1%), runtime (1.415SI), AUC (3.5%), recall (2%),TNR (2%),TPR (5%),FNR (15%), error rate (35%), and FPR (52%) on Android application sets. These improvements stem from optimizing the convolutional network with gradient boosting machine during the malware detection phase.

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Section: Related Workmentioning

confidence: 99%

Gradient-Boosted Convolutional Neural Network Malware Detection Technique for Android Smartphones

Chimeleze

Jamil

Teh

et al. 2022

Preprint

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show abstract

“…The general purpose of their researches was to achieve good tradeoff between the performance and the computational complexity of the system. Additionally, various low-complexity detection schemes have been developed in order to reach near-optimal performance [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Damped Jacobi Methods Based on Two Different Matrices for Signal Detection in Massive MIMO Uplink

Naceur

2021

J. Microw. Optoelectron. Electromagn. Appl.

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For massive multiple-input multiple-output (m-MIMO) uplink, the performances of the linear minimum mean-square error (MMSE) detector are considered near optimal, and they occupy benchmark place for most linear iterative detectors. However, the MMSE algorithm is known by its load computational complexity due to the implication of large-scale matrix inversions, and in other hand, iterative methods are often preferred in signal detection because of its low complexity. In this paper, we propose a New Damped Jacobi (NDJ) detector in order to improve the performance of the classical Jacobi linear algorithm. Starting from the classical Jacobi technique to our new proposal, we go through the development of two variants; one uses a damping factor and the other uses a stair-matrix. However, the NDJ incorporates a damping factor in its construction and basing also on stair matrix instead of diagonal matrix. The performances in terms of convergence and low complexity of each Jacobi variant studied in this paper are analyzed. Finally, some simulation examples are given to illustrate the advantages of the new proposed algorithm.

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Performance analysis and optimization of a hybrid TSR–PSR protocol for AF, DF and hybrid AF–DF relaying under Weibull fading

Chowdhury

Sadhu²,

Thakur³

et al. 2022

Telecommun Syst

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Signal Detection Based on Parallel Group Detection Algorithm for High-Load Massive MIMO Systems

Cited by 6 publications

References 19 publications

Gradient-Boosted Convolutional Neural Network Malware Detection Technique for Android Smartphones

Gradient-Boosted Convolutional Neural Network Malware Detection Technique for Android Smartphones

Damped Jacobi Methods Based on Two Different Matrices for Signal Detection in Massive MIMO Uplink

Performance analysis and optimization of a hybrid TSR–PSR protocol for AF, DF and hybrid AF–DF relaying under Weibull fading

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