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Maximizing dirty-paper coding rate of RIS-assisted multi-user MIMO broadcast channels

Intelligent and Converged Networks 2022年第1期3卷 64-73页

作者： Mohamed A.Elmossallamy radwa sultan Karim G.Seddik Geoffery Ye Li Zhu Han Qualcomm Inc. BoxboroughMA 01719USA Electrical and Computer Engineering Department and the Computer Science Department University of HoustonHoustonTX 77004USA. the Electrical and Computer Engineering Department Manhattan CollegeRiverdaleNY 10471USA. Electronics and Communications Engineering Department American University in CairoNew Cairo 11835Egypt. Department of Electrical and Electronic Engineering Imperial College LondonLondonSW72AZUK

We consider a downlink multi-user scenario and investigate the use of reconfigurable intelligent surfaces(RISs)to maximize the dirty-paper-coding(DPC)sum rate of the RIS-assisted broadcast *** from prior works,which maximize the rate achievable by linear precoders,we assume a capacity-achieving DPC scheme is employed at the transmitter and optimize the transmit covariances and RIS reflection coefficients to directly maximize the sum capacity of the broadcast *** propose an optimization algorithm that iteratively alternates between optimizing the transmit covariances using convex optimization and the RIS reflection coefficients using Riemannian manifold *** results show that the proposed technique can be used to effectively improve the sum capacity in a variety of scenarios compared to benchmark schemes.

关键词： broadcast channels dirty-paper coding multiple-input-multiple-output(MIMO) reconfigurable intelligent surfaces

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Hybrid Deep Learning Enabled Intrusion Detection in Clustered IIoT Environment

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Computers, Materials & Continua 2022年第8期72卷 3763-3775页

作者： radwa Marzouk Fadwa Alrowais Noha Negm Mimouna Abdullah Alkhonaini Manar Ahmed Hamza Mohammed Rizwanullah Ishfaq Yaseen Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Industrial Internet of Things(IIoT)is an emerging field which connects digital equipment as well as services to physical *** detection systems(IDS)can be designed to protect the system from intrusions or *** this view,this paper presents a novel hybrid deep learning with metaheuristics enabled intrusion detection(HDL-MEID)technique for clustered IIoT *** HDL-MEID model mainly intends to organize the IIoT devices into clusters and enabled secure ***,the HDL-MEID technique designs a new chaotic mayfly optimization(CMFO)based clustering approach for the effective choice of the Cluster Heads(CH)and organize ***,equilibrium optimizer with hybrid convolutional neural network long short-term memory(HCNNLSTM)based classification model is derived to identify the existence of the intrusions in the IIoT *** experimental analysis is performed to highlight the enhanced outcomes of the HDL-MEID technique and the results were investigated under different *** experimental results highlight the supremacy of the proposed HDL-MEID technique over recent state-of-the-art techniques.

关键词： Industrial internet of things security intrusion detection classification deep learning

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Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems

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Computers, Materials & Continua 2022年第8期72卷 2783-2795页

作者： Fadwa Alrowais Ahmed S.Almasoud radwa Marzouk Fahd N.Al-Wesabi Anwer Mustafa Hilal Mohammed Rizwanullah Abdelwahed Motwakel Ishfaq Yaseen Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRafha StreetRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems Faculty of Computer and Information TechnologySana’a University61101Yemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Mobile edge computing(MEC)provides effective cloud services and functionality at the edge device,to improve the quality of service(QoS)of end users by offloading the high computation ***,the introduction of deep learning(DL)and hardware technologies paves amethod in detecting the current traffic status,data offloading,and cyberattacks in *** study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC(AIMDO-SMEC)*** proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks(SNN)to determine the traffic status in the MEC ***,an adaptive sampling cross entropy(ASCE)technique is utilized for data offloading in MEC ***,the modified salp swarm algorithm(MSSA)with extreme gradient boosting(XGBoost)technique was implemented to identification and classification of cyberattack that exist in the MEC *** examining the enhanced outcomes of the AIMDO-SMEC technique,a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDOSMEC technique with the minimal completion time of tasks(CTT)of 0.680.

关键词： Data offloading mobile edge computing security machine learning artificial intelligence XGBoost salp swarm algorithm

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