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Optimizing Age of Information Through Aerial Reconfigurable Intelligent Surfaces: A Deep Reinforcement Learning Approach
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). In order to maintain the freshness of relayed information, an optim...
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Published in: | IEEE transactions on vehicular technology 2021-04, Vol.70 (4), p.3978-3983 |
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container_title | IEEE transactions on vehicular technology |
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creator | Samir, Moataz Elhattab, Mohamed Assi, Chadi Sharafeddine, Sanaa Ghrayeb, Ali |
description | 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). In order to maintain the freshness of relayed information, an optimization problem with the objective of minimizing the expected sum Age-of-Information (AoI) is formulated to optimize the altitude of the UAV, the communication schedule, and phases-shift of RIS elements. In the absence of prior knowledge of the activation pattern of the IoTDs, proximal policy optimization algorithm is developed to solve this mixed-integer non-convex optimization problem. Numerical results show that our proposed algorithm outperforms all others in terms of AoI. |
doi_str_mv | 10.1109/TVT.2021.3063953 |
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subjects | Algorithms AoI Computational geometry Convexity Delays Fading channels Internet of Things IoT Mixed integer Optimization PPO Reconfigurable intelligent surfaces Relays Reliability RIS Schedules scheduling UAV altitude Unmanned aerial vehicles Wireless networks |
title | Optimizing Age of Information Through Aerial Reconfigurable Intelligent Surfaces: A Deep Reinforcement Learning Approach |
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