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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on vehicular technology 2021-04, Vol.70 (4), p.3978-3983
Main Authors: Samir, Moataz, Elhattab, Mohamed, Assi, Chadi, Sharafeddine, Sanaa, Ghrayeb, Ali
Format: Article
Language:English
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
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2021.3063953