Published ETD Collection
Permanent URI for this collectionhttps://hdl.handle.net/10657/2
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Browsing Published ETD Collection by Author "Abdelhadi, Ahmed"
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Item A Back Propagation Based Spiking Neural Network Approach for Intelligent Link Decisions In Satellite Communication(2021-05) Visweswaran, Meenakshi; Lent, Ricardo; Gurkan, Deniz; Abdelhadi, AhmedA Spiking Neural Network (SNN) with neuromorphic architecture for optimal link decisions is put forward in this paper. SNNs can adapt to the various changes in the working environment quickly, for maintenance or advancement of the selected performance metrics. Such results can be appealing for satellite networks with orbital operations involving either stationary or manned aids, which would provide directions for autonomy in CN decisions. The satellite on-board processing capabilities, traditionally, have been a limiting factor for advanced satellite communication strategies. Additionally, with deep space explorations rising, the demand for bandwidth is increasing, which can be achieved by making communication systems more efficient. Manual updating procedures for satellite operations gives rise to chances of configuration errors. Since AI has been showing continuous improvements and glorious performances, when applying it to convert manual operations to intelligent ones, some errors can be avoided. In scenarios where the delay time of an operator responding is considerable, the spacecraft must be able to autonomously make decisions. Intelligent systems can help improve spacecraft reliability by being trained to react to unexpected situations and guide the spacecraft to safer operational states with autonomous decision-making. This serves as an apt area to apply an SNN model for a lighter space network on a first-hop level. This literature will be focused on enabling flexible routing for link selection with the help of Spiking Neural Networks. This path selection problem is approached by applying Spike Neural Network (SNN) to classify satellite downlinks based on link cost to improve learning, and later analyze the classification and link decision capabilities of the network with respect to a traditional neural network. The spiking network has inbuilt Back Propagation (BP) implemented in the framework, Nengo. The system managed to achieve better accuracy even when activation was provided in hidden layer instead of output layers. Tweaking the firing rates, epochs and batch size of the data might yield better results. For the LEO scenario, a maximum accuracy of 86% was obtained for synthetic data using SNN and for the GEO scenario, a maximum of 98.5% was obtained.Item Jitters in Operating Systems for the Internet of Things(2018-12) Pandey, Navneet 1992-; Gnawali, Omprakash; Alipour, M. Amin; Abdelhadi, AhmedThe Internet of Things (IoT) is an extension of the internet into the physical world through the use of sensing, actuation, control, and interaction with embedded devices. A large number of IoT devices are being deployed in the world. The emerging applications involving IoT require reliable network connectivity. Latency is one of the most critical network performance metric that will determine the user experience with IoT applications. There are two aspects of latency metric – the overall delay and the jitters. Most of the focus is on low delay but many applications, especially the ones with real-time-like requirements, also need low jitters in latency to have predictable protocols or interactions at the system level. This thesis presents a study of jitters in the IoT operating systems observed through various networking-related operations and systems. The execution and performance of the application can be greatly affected by the characteristics of an Operating System (OS) in the IoT system. This thesis presents a study of network stack performance, layer-wise packet trace, and its analysis. The key focus of the analysis is identifying the presence of jitters in the IoT OS and the contributing factors behind their presence. The approach taken by this thesis is that it performs a series of measurement studies of basic applications on IoT hardware and OS platforms. We evaluate this study with two OS – RIOT and Contiki OS and two IoT hardware platforms – IoTLAB-M3 open node and TelosB. This thesis provides guidance on the achievable network performance and characteristics for different system requirements of IoT applications.Item Practical implementation of modulation classification and adversarial attacks using Universal Software Radio Peripheral with deep learning(2023-05-15) SULTANA, SALMA; Abdelhadi, Ahmed; Yuan, Xiaojing; Kumar, Akshay; Agarwal, NitinDeep learning (DL) has proven to be highly effective in solving classification problems, making it an ideal tool for identifying unknown modulation signals. This research aims to accurately classify signal modulation classes in over-the-air approaches using software-defined radios (SDR). The study examines a wireless communication system with a transmitter (Tx) and receiver (Rx). The process of Modulation Classification at the Rx is achieved by utilizing deep learning models such as the Convolutional Neural Network (CNN), Residual Network (ResNet), and a combination of CNN and Long Short-Term Memory (CLSTM). These models classify the modulated signals, like BPSK, QPSK, 8PSK, and 16QAM. To introduce adversarial attacks, an adversary (Ad) is added to the communication system. In this report, the Fast-Gradient Sign method (FGSM) adversarial attack method is employed to create fake signals that deceive DL-based classifiers and lead to significant reductions in the accuracies of three DL models. To combat adversarial attacks, a generative-adversarial network (GAN)-based defense is proposed to enhance the accuracy of the DL models.Item Reconfigurable Intelligent Surfaces: Channel Estimation and Applications in Future Wireless Networks(2023-08) Shtaiwi, Eyad; Han, Zhu; Abdelhadi, Ahmed; Pan, Miao; Prasad, Saurabh; Al-Dhahir, Naofal; Debbah, MérouaneReconfigurable Intelligent Surfaces (RISs) are planar structures comprising passive, low-cost reflecting elements that efficiently manipulate the electromagnetic (EM) propagation environment through phase and amplitude adjustments, enhancing wireless system performance. RIS passive beamforming eliminates the need for complex signal processing or RF chains, offering benefits like improved signal-to-interference noise ratio (SINR), enhanced link quality-of-service (QoS), and reduced energy consumption. However, the limited signal processing capability of RIS-assisted wireless networks presents design challenges. This dissertation comprises two main parts, addressing channel estimation schemes for RIS-assisted MIMO systems and practical RIS implementation to optimize MIMO Radar and communication coexistence systems, respectively. In the first part, we introduce an innovative algorithm for estimating the composite channel, separate RIS-based channels, and the direct channel within the RIS-assisted system. By dividing the RIS surface into smaller sub-RIS units and controlling the phase shifts, we successfully estimate the overall channel. Additionally, we propose a straightforward passive pilot sequence scheduling scheme to jointly adjust phase shift coefficients. Next, we present a two-stage channel estimation approach for RIS-assisted mmWave communication systems. The first stage identifies active users and efficiently estimates their channel parameters using sparsity. The second stage utilizes partial Channel State Information (CSI) and spatial-temporal correlation to estimate inactive users' channel coefficients, adopting a spatial-temporal-spectral (STS) framework based on convolutional neural networks (CNNs). The second part addresses enhancing a Multiple-Input Multiple-Output (MIMO) Radar and Communication Coexistence (RCC) system using RIS deployment. To mitigate interference in a Multi-User (MU)-MIMO RCC setup, we propose a strategic RIS deployment scheme, leveraging their passive beamforming capabilities. This dissertation provides insights into RIS-assisted MIMO channel estimation and practical RIS implementation for wireless system design, advancing RIS-assisted wireless communication across various applications.