Age of Information-Oriented Probabilistic Link Scheduling for Device-to-Device Networks

TL;DR

This paper develops an age-aware link scheduling policy for D2D networks to minimize the average age of information, using a combination of Lyapunov optimization and graph neural networks for scalable, real-time decision-making.

Contribution

It introduces a novel age-aware randomized policy utilizing MPNNs to optimize AoI in D2D networks, addressing nonconvexity and scalability challenges.

Findings

Proposed policy outperforms baseline methods in simulations.

The method effectively handles interference and dynamics in D2D networks.

Scalability is validated through simulation results.

Abstract

This paper focuses on optimizing the long-term average age of information (AoI) in device-to-device (D2D) networks through age-aware link scheduling. The problem is naturally formulated as a Markov decision process (MDP). However, finding the optimal policy for the formulated MDP in its original form is challenging due to the intertwined AoI dynamics of all D2D links. To address this, we propose an age-aware stationary randomized policy that determines the probability of scheduling each link in each time slot based on the AoI of all links and the statistical channel state information among all transceivers. By employing the Lyapunov optimization framework, our policy aims to minimize the Lyapunov drift in every time slot. Nonetheless, this per-slot minimization problem is nonconvex due to cross-link interference in D2D networks, posing significant challenges for real-time decision-making. After analyzing the permutation equivariance property of the optimal solutions to the per-slot problem, we apply a message passing neural network (MPNN), a type of graph neural network that also exhibits permutation equivariance, to optimize the per-slot problem in an unsupervised learning manner. Simulation results demonstrate the superior performance of the proposed age-aware stationary randomized policy over baselines and validate the scalability of our method.