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37 results on '"El-Malek, Ahmed H. Abd"'

7. Performance Analysis and Optimization of RIS-Assisted Networks in Nakagami-m Environment

Author

Samuh, Monjed H., Salhab, Anas M., and El-Malek, Ahmed H. Abd

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

This work studies and optimizes the performance of reconfigurable intelligent surface (RIS)-aided networks in Nakagami-m fading environment. First, accurate closed-form approximations for the channel distributions are derived. Then, closed-form formulas for the system outage probability, average symbol error probability (ASEP), and the channel capacity are obtained. Furthermore, we provide three different optimization approaches for finding the optimum number of reflecting elements to achieve a target outage probability. At the high signal-to-noise ratio (SNR) regime, a closed-form expression for the asymptotic outage probability is obtained to give more insights into the system performance. Results show that the considered system can achieve a diversity order of (a+1)/2, where a is a function of the Nakagami-m fading parameter m and the number of reflecting elements N. Moreover, findings show that m is more influential on the diversity order than N. Finally, the achieved expressions are applicable to non-integer values of m and any number of meta-surface elements N.

Published
2020

13. Performance Analysis and Path-Planning for Self-Energized UAV-Assisted Relay Networks

Author

Aboulhassan, Mohamed A., El-Malek, Ahmed H. Abd, Salhab, Anas M., and Zummo, Salam A.

Abstract

This article studies performance analysis for a novel unmanned aerial vehicles (UAVs) selection algorithm for UAV-assisted relay networks. Several UAVs are located randomly between the transmitter and receiver base stations in the proposed model. Assuming no direct link between the transmitter and receiver, only one UAV is selected to act as a relay. Furthermore, we assume that the UAVs are energized from a dedicated power base station. The UAV selection mechanism is performed over two phases. In phase one, the UAVs that have succeeded in harvesting energy greater than a predefined threshold are eligible to be selected. Whereas in phase two, the UAV with the highest signal-to-noise ratio is selected to act as a relay. We derive closed-form expressions for the total outage probability, average throughput, and average symbol error probability under several practical assumptions, such as the nonlinear energy harvesting model, random UAV locations, and Nakagami-$m$ fading channel models. Moreover, we propose a UAV localization approach using deep reinforcement learning (DRL) for the selected UAV. The localization problem is found to be nonconvex. Thus, a DRL approach is used to find the optimum UAV trajectory. The simulation results reveal that the proposed localization and selection approaches outperform conventional techniques in the literature. Furthermore, findings show the practicality and validity of the derived closed-form expressions.

Published
2024
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