Mostafa M. Fouda | Idaho State University (original) (raw)

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Papers by Mostafa M. Fouda

Research paper thumbnail of Polyhedron Optimization for Power Allocation of Cell-Free based IRS System

Research paper thumbnail of Advancing Malaria Identification from Microscopic Blood Smears Using Hybrid Deep Learning Frameworks

Research paper thumbnail of Underwater Target Detection using Deep Learning: Methodologies, Challenges, Applications and Future Evolution

IEEE Access, Dec 31, 2023

Research paper thumbnail of Vehicles communications handover in 5G: A survey

Research paper thumbnail of Dynamic Modeling and Control of Hybrid AC/DC Microgrid With Green Hydrogen Energy Storage

Research paper thumbnail of Energy Management and Techno-Economic Optimization of an Isolated Hybrid AC/DC Microgrid with Green Hydrogen Storage System

Research paper thumbnail of AI-Aided Height Optimization for NOMA-UAV Networks

Research paper thumbnail of AI Aided Noise Processing of Spintronic Based IoT Sensor for Magnetocardiography Application

arXiv (Cornell University), Nov 8, 2019

Research paper thumbnail of AI-Enabled UAV Communications: Challenges and Future Directions

Research paper thumbnail of Explainable Transfer Learning-Based Deep Learning Model for Pelvis Fracture Detection

International Journal of Intelligent Systems, Jun 24, 2023

Research paper thumbnail of Detection of False-Reading Attacks in the AMI Net-Metering System

arXiv (Cornell University), Dec 2, 2020

Research paper thumbnail of Enhanced FFR Scenario for Pilot Contamination Mitigation in 5G Systems with Massive Mimo

Benha Journal of Applied Sciences, Mar 1, 2017

Research paper thumbnail of A spatiotemporal scenario to mitigate pilot contamination in 5G massive MIMO systems

Massive multiple-input multiple-output (MIMO) or large-scale MIMO is the magical technology that ... more Massive multiple-input multiple-output (MIMO) or large-scale MIMO is the magical technology that can provide the required large system capacity in the future fifth generation (5G) cellular networks. Massive MIMO is characterized by a large number of base station (BS) antennas, which makes it less susceptible to noise and fast fading. Unfortunately, this large antennas number does not enable the massive MIMO to get rid of the inter-cell interference originated from the problem of pilot contamination. In this article, we propose a spatiotemporal scenario that can mitigate the pilot contamination effects by avoiding synchronous pilot transmission in the time domain and following a pilot allocation algorithm in the spatial domain. We show that our proposed scenario provides a reduced level of inter-cell interference, which eventually gives it the ability to enhance the minimum and the mean achievable capacities per terminal.

Research paper thumbnail of A novel vision to mitigate pilot contamination in massive MIMO-based 5G networks

Massive multiple-input multiple-output (MIMO) is one of the promising technologies to be used in ... more Massive multiple-input multiple-output (MIMO) is one of the promising technologies to be used in the future fifth generation (5G) wireless networks for its capability to increase the system throughput, improve spectral efficiency and energy efficiency. To achieve these benefits of the massive MIMO, there are many challenges that need to be handled. One of these challenges is the pilot contamination problem that arises from reusing pilot sequences among the system cells. Since this problem is considered as a bottleneck of the system performance, it attracted many researchers who proposed schemes to solve it. In this paper, we propose a new scheme that mitigates its effect by adopting both asynchronous pilot transmission (APT) and fractional pilot reuse (FPR). We compare the performance of our proposed scheme with other schemes using computer simulations and show that our proposed scheme enhances the performance of the time division duplex (TDD) multi-cell massive MIMO systems in terms of the signal to interference and noise ratio (SINR) and capacity.

Research paper thumbnail of LSTM-Autoencoder Deep Learning Technique for PAPR Reduction in Visible Light Communication

Research paper thumbnail of Deep Learning-Based Fault Classification and Location for Underground Power Cable of Nuclear Facilities

IEEE Access, 2022

Worldwide, Nuclear Power Plants (NPPs) must have higher security protection and precise fault det... more Worldwide, Nuclear Power Plants (NPPs) must have higher security protection and precise fault detection systems, especially underground power cable faults, to avoid causing national disasters and keep on safe national ratios of electricity production. Hence, this paper proposes an automatic, effective, and accurate Deep Learning (DL)-based fault classification and location technique for these cables via a One-dimensional Convolutional Neural Network (1D-CNN) and a Binary Support Vector Machine (BSVM). The proposed approach includes four main steps: data collection, feature extraction and reduction, fault detection, and fault classification and location. Signal collection from the underground cable's sending end is performed via the Alternating Transient Program/Electromagnetic Transient Program (ATP/EMTP). Feature extraction and reduction are performed via Fractional Discrete Cosine Transform (FrDCT) and Singular Value Decomposition (SVD) methods. Fault detection is performed through leveraging BSVM with the linear Kernel method in the third step. Finally, this permits 1D-CNN to classify the fault type and locate it. Simulation results confirmed the efficiency of our proposed method, especially for 11kV underground cable faults, including different fault resistances and inception angles. Moreover, the proposed technique is applicable in real-time scenarios with a 99.6% accuracy rate, 0.15sec lowest execution time, and 0.095% maximum error rate for fault location at fractional factor (α) equals to 0.8.

Research paper thumbnail of Short-Term Traffic Prediction Using Deep Learning Long Short-Term Memory: Taxonomy, Applications, Challenges, and Future Trends

Research paper thumbnail of Secure and Efficient Communication in Smart Grids

Energies

This Special Issue on “Secure and Efficient Communication in Smart Grids” received a total of 11 ... more This Special Issue on “Secure and Efficient Communication in Smart Grids” received a total of 11 submitted articles, of which 5 were accepted and published after each passing an independent peer-review process [...]

Research paper thumbnail of Ergodic Performance Analysis of Double Intelligent Reflecting Surfaces-Aided NOMA–UAV Systems with Hardware Impairment

Drones

In this work, we design an intelligent reflecting surface (IRS)-assisted Internet of Things (IoT)... more In this work, we design an intelligent reflecting surface (IRS)-assisted Internet of Things (IoT) by enabling non-orthogonal multiple access (NOMA) and unmanned aerial vehicles (UAV) approaches. We pay attention to studying the achievable rates for the ground users. A practical system model takes into account the presence of hardware impairment when Rayleigh and Rician channels are adopted for the IRS–NOMA–UAV system. Our main findings are presented to showcase the exact expressions for achievable rates, and then we derive their simple approximations for a more insightful performance evaluation. The validity of these approximations is demonstrated using extensive Monte Carlo simulations. We confirm the achievable rate improvement decided by main parameters such as the average signal to noise ratio at source, the position of IRS with respect to the source and destination and the number of IRS elements. As a suggestion for the deployment of a low-cost IoT system, the double-IRS model ...

Research paper thumbnail of Employing Machine Learning and IoT for Earthquake Early Warning System in Smart Cities

Energies

An earthquake early warning system (EEWS) should be included in smart cities to preserve human li... more An earthquake early warning system (EEWS) should be included in smart cities to preserve human lives by providing a reliable and efficient disaster management system. This system can alter how different entities communicate with one another using an Internet of Things (IoT) network where observed data are handled based on machine learning (ML) technology. On one hand, IoT is employed in observing the different measures of EEWS entities. On the other hand, ML can be exploited to analyze these measures to reach the best action to be taken for disaster management and risk mitigation in smart cities. This paper provides a survey on the different aspects required for that EEWS. First, the IoT system is generally discussed to provide the role it can play for EEWS. Second, ML models are classified into linear and non-linear ones. Third, the evaluation metrics of ML models are addressed by focusing on seismology. Fourth, this paper exhibits a taxonomy that includes the emerging ML and IoT e...

Research paper thumbnail of Polyhedron Optimization for Power Allocation of Cell-Free based IRS System

Research paper thumbnail of Advancing Malaria Identification from Microscopic Blood Smears Using Hybrid Deep Learning Frameworks

Research paper thumbnail of Underwater Target Detection using Deep Learning: Methodologies, Challenges, Applications and Future Evolution

IEEE Access, Dec 31, 2023

Research paper thumbnail of Vehicles communications handover in 5G: A survey

Research paper thumbnail of Dynamic Modeling and Control of Hybrid AC/DC Microgrid With Green Hydrogen Energy Storage

Research paper thumbnail of Energy Management and Techno-Economic Optimization of an Isolated Hybrid AC/DC Microgrid with Green Hydrogen Storage System

Research paper thumbnail of AI-Aided Height Optimization for NOMA-UAV Networks

Research paper thumbnail of AI Aided Noise Processing of Spintronic Based IoT Sensor for Magnetocardiography Application

arXiv (Cornell University), Nov 8, 2019

Research paper thumbnail of AI-Enabled UAV Communications: Challenges and Future Directions

Research paper thumbnail of Explainable Transfer Learning-Based Deep Learning Model for Pelvis Fracture Detection

International Journal of Intelligent Systems, Jun 24, 2023

Research paper thumbnail of Detection of False-Reading Attacks in the AMI Net-Metering System

arXiv (Cornell University), Dec 2, 2020

Research paper thumbnail of Enhanced FFR Scenario for Pilot Contamination Mitigation in 5G Systems with Massive Mimo

Benha Journal of Applied Sciences, Mar 1, 2017

Research paper thumbnail of A spatiotemporal scenario to mitigate pilot contamination in 5G massive MIMO systems

Massive multiple-input multiple-output (MIMO) or large-scale MIMO is the magical technology that ... more Massive multiple-input multiple-output (MIMO) or large-scale MIMO is the magical technology that can provide the required large system capacity in the future fifth generation (5G) cellular networks. Massive MIMO is characterized by a large number of base station (BS) antennas, which makes it less susceptible to noise and fast fading. Unfortunately, this large antennas number does not enable the massive MIMO to get rid of the inter-cell interference originated from the problem of pilot contamination. In this article, we propose a spatiotemporal scenario that can mitigate the pilot contamination effects by avoiding synchronous pilot transmission in the time domain and following a pilot allocation algorithm in the spatial domain. We show that our proposed scenario provides a reduced level of inter-cell interference, which eventually gives it the ability to enhance the minimum and the mean achievable capacities per terminal.

Research paper thumbnail of A novel vision to mitigate pilot contamination in massive MIMO-based 5G networks

Massive multiple-input multiple-output (MIMO) is one of the promising technologies to be used in ... more Massive multiple-input multiple-output (MIMO) is one of the promising technologies to be used in the future fifth generation (5G) wireless networks for its capability to increase the system throughput, improve spectral efficiency and energy efficiency. To achieve these benefits of the massive MIMO, there are many challenges that need to be handled. One of these challenges is the pilot contamination problem that arises from reusing pilot sequences among the system cells. Since this problem is considered as a bottleneck of the system performance, it attracted many researchers who proposed schemes to solve it. In this paper, we propose a new scheme that mitigates its effect by adopting both asynchronous pilot transmission (APT) and fractional pilot reuse (FPR). We compare the performance of our proposed scheme with other schemes using computer simulations and show that our proposed scheme enhances the performance of the time division duplex (TDD) multi-cell massive MIMO systems in terms of the signal to interference and noise ratio (SINR) and capacity.

Research paper thumbnail of LSTM-Autoencoder Deep Learning Technique for PAPR Reduction in Visible Light Communication

Research paper thumbnail of Deep Learning-Based Fault Classification and Location for Underground Power Cable of Nuclear Facilities

IEEE Access, 2022

Worldwide, Nuclear Power Plants (NPPs) must have higher security protection and precise fault det... more Worldwide, Nuclear Power Plants (NPPs) must have higher security protection and precise fault detection systems, especially underground power cable faults, to avoid causing national disasters and keep on safe national ratios of electricity production. Hence, this paper proposes an automatic, effective, and accurate Deep Learning (DL)-based fault classification and location technique for these cables via a One-dimensional Convolutional Neural Network (1D-CNN) and a Binary Support Vector Machine (BSVM). The proposed approach includes four main steps: data collection, feature extraction and reduction, fault detection, and fault classification and location. Signal collection from the underground cable's sending end is performed via the Alternating Transient Program/Electromagnetic Transient Program (ATP/EMTP). Feature extraction and reduction are performed via Fractional Discrete Cosine Transform (FrDCT) and Singular Value Decomposition (SVD) methods. Fault detection is performed through leveraging BSVM with the linear Kernel method in the third step. Finally, this permits 1D-CNN to classify the fault type and locate it. Simulation results confirmed the efficiency of our proposed method, especially for 11kV underground cable faults, including different fault resistances and inception angles. Moreover, the proposed technique is applicable in real-time scenarios with a 99.6% accuracy rate, 0.15sec lowest execution time, and 0.095% maximum error rate for fault location at fractional factor (α) equals to 0.8.

Research paper thumbnail of Short-Term Traffic Prediction Using Deep Learning Long Short-Term Memory: Taxonomy, Applications, Challenges, and Future Trends

Research paper thumbnail of Secure and Efficient Communication in Smart Grids

Energies

This Special Issue on “Secure and Efficient Communication in Smart Grids” received a total of 11 ... more This Special Issue on “Secure and Efficient Communication in Smart Grids” received a total of 11 submitted articles, of which 5 were accepted and published after each passing an independent peer-review process [...]

Research paper thumbnail of Ergodic Performance Analysis of Double Intelligent Reflecting Surfaces-Aided NOMA–UAV Systems with Hardware Impairment

Drones

In this work, we design an intelligent reflecting surface (IRS)-assisted Internet of Things (IoT)... more In this work, we design an intelligent reflecting surface (IRS)-assisted Internet of Things (IoT) by enabling non-orthogonal multiple access (NOMA) and unmanned aerial vehicles (UAV) approaches. We pay attention to studying the achievable rates for the ground users. A practical system model takes into account the presence of hardware impairment when Rayleigh and Rician channels are adopted for the IRS–NOMA–UAV system. Our main findings are presented to showcase the exact expressions for achievable rates, and then we derive their simple approximations for a more insightful performance evaluation. The validity of these approximations is demonstrated using extensive Monte Carlo simulations. We confirm the achievable rate improvement decided by main parameters such as the average signal to noise ratio at source, the position of IRS with respect to the source and destination and the number of IRS elements. As a suggestion for the deployment of a low-cost IoT system, the double-IRS model ...

Research paper thumbnail of Employing Machine Learning and IoT for Earthquake Early Warning System in Smart Cities

Energies

An earthquake early warning system (EEWS) should be included in smart cities to preserve human li... more An earthquake early warning system (EEWS) should be included in smart cities to preserve human lives by providing a reliable and efficient disaster management system. This system can alter how different entities communicate with one another using an Internet of Things (IoT) network where observed data are handled based on machine learning (ML) technology. On one hand, IoT is employed in observing the different measures of EEWS entities. On the other hand, ML can be exploited to analyze these measures to reach the best action to be taken for disaster management and risk mitigation in smart cities. This paper provides a survey on the different aspects required for that EEWS. First, the IoT system is generally discussed to provide the role it can play for EEWS. Second, ML models are classified into linear and non-linear ones. Third, the evaluation metrics of ML models are addressed by focusing on seismology. Fourth, this paper exhibits a taxonomy that includes the emerging ML and IoT e...