Search Results for author: Sanaa Sharafeddine

Found 6 papers, 1 papers with code

Multi-IRS Aided Mobile Edge Computing for High Reliability and Low Latency Services

no code implementations14 Dec 2023 Elie El Haber, Mohamed Elhattab, Chadi Assi, Sanaa Sharafeddine, Kim Khoa Nguyen

Harnessing the IRS's potential in enhancing the performance of edge computation offloading, in this paper, we study the optimized use of a system of multi-IRS along with the design of the offloading (to an edge with multi MECs) and resource allocation parameters for the purpose of minimizing the devices' energy consumption considering 5G services with stringent latency and reliability requirements.

Edge-computing

RIS-Assisted UAV for Timely Data Collection in IoT Networks

no code implementations31 Mar 2021 Ahmed Al-Hilo, Moataz Samir, Mohamed Elhattab, Chadi Assi, Sanaa Sharafeddine

This integrated problem brings challenges related to the configuration of the phase shift elements of the RIS, the scheduling of IoTDs transmissions as well as the trajectory of the UAV.

Decision Making Edge-computing +2

Reconfigurable Intelligent Surface Enabled Vehicular Communication: Joint User Scheduling and Passive Beamforming

no code implementations28 Jan 2021 Ahmed Al-Hilo, Moataz Samir, Mohamed Elhattab, Chadi Assi, Sanaa Sharafeddine

Therefore, the joint problem of RSU resource scheduling and RIS passive beamforming or phase-shift matrix is formulated as an optimization problem with the objective of maximizing the minimum average bit rate.

Scheduling

Optimizing Age of Information Through Aerial Reconfigurable Intelligent Surfaces: A Deep Reinforcement Learning Approach

no code implementations9 Nov 2020 Moataz Samir, Mohamed Elhattab, Chadi Assi, Sanaa Sharafeddine, Ali Ghrayeb

We investigate the benefits of integrating unmanned aerial vehicles (UAVs) with reconfigurable intelligent surface (RIS) elements to passively relay information sampled by Internet of Things devices (IoTDs) to the base station (BS).

Reinforcement Learning (RL)

UAV Trajectory Planning for Data Collection from Time-Constrained IoT Devices

1 code implementation IEEE Transactions on Wireless Communications 2019 Moataz Samir, Sanaa Sharafeddine, Chadi M. Assi, Tri Minh Nguyen, Ali Ghrayeb

To this end, we jointly optimize the trajectory of a UAV and the radio resource allocation to maximize the number of served IoT devices, where each device has its own target data upload deadline.

Benchmarking Trajectory Planning

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