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ECE-Theses-MSc

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Browsing ECE-Theses-MSc by Author "El-Kholy, Enji Mamdouh Rashad Mahmoud,"

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    Performance evaluation of intelligent reflecting surface in heterogeneous wireless networks : (5G and beyond) /
    El-Kholy, Enji Mamdouh Rashad Mahmoud,; Supervisor : Fawzy Ibrahim Abd El-Ghany, Mahmoud Mohamed El-Mesalawy, Mehaseb Ahmed Mehaseb. Includes Arabic Summary.
    Simultaneous Wireless Information and Power Transfer (SWIPT) is recognized as a promising technique for addressing the energy constraints in wireless communication systems. Meanwhile, Intelligent Reflecting Surfaces (IRS) can customize wireless channels to enhance the spectral and energy efficiency of wireless communication systems. Integrating IRS into SWIPT systems can enable long-distance wireless communication with high data rates, device density, reliability, and energy endurance, particularly in the forthcoming Sixth Generation (6G) era. This thesis investigates a Multiple-Input Single-Output (MISO) IRS-assisted SWIPT system. This system involves a Base Station (BS) mounted on an Unmanned Aerial Vehicle (UAV) that concurrently sends information to Information Decoding Receivers (IDRs) and transfers energy to Energy Harvesting Receivers (EHRs). Unlike conventional SWIPT systems with fixed transmitters, UAV-mounted BSs offer flexible wireless service delivery to ground users. The main objective of the proposed system is to maximize the spectral efficiency of the IDRs by simultaneously optimizing the phase shifts of the reflecting elements at the IRS and the precoding vectors at the UAV. To achieve this, the thesis proposes the utilization of Dinkelbach’s algorithm, which allows the optimization of all the variables simultaneously with an acceptable complexity. MATLAB-based simulation results show that integrating the IRS into the SWIPT system enhances the spectral efficiency of IDRs by 60% and the energy efficiency of EHRs by almost 20 μW compared to the system without IRS. Furthermore, numerical analysis reveals that the proposed method significantly exceeds the performance of the AO-based SCA algorithm by approximately 78%