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

TL;DR

This paper proposes a deep reinforcement learning approach to optimize UAV altitude, communication scheduling, and RIS phase shifts to minimize the Age of Information in IoT networks, demonstrating superior performance.

Contribution

It introduces a novel deep reinforcement learning method to jointly optimize UAV and RIS parameters for AoI minimization without prior IoTD activation knowledge.

Findings

Proposed algorithm outperforms existing methods in AoI reduction.

Effective joint optimization of UAV altitude, RIS phases, and scheduling.

Demonstrated significant AoI improvements through numerical simulations.

Abstract

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.