AoI-Driven Queue Management and Power Control in V2V Networks: A GNN-Enhanced MARL Approach

TL;DR

This paper presents a novel GNN-enhanced MARL framework for AoI-aware queue management and power control in V2V networks, improving status update freshness under resource constraints.

Contribution

It introduces a joint packet dropping and power control optimization with a hybrid action space and leverages GNNs for topology-aware interference modeling within a MARL setup.

Findings

Significantly reduces average AoI across various network conditions

Outperforms baseline methods in AoI minimization

Effectively captures network topology dependencies without frequent message exchange

Abstract

Queue management and resource allocation play a critical role in enabling cooperative status awareness in vehicular networks. This paper investigates the problem of age of information (AoI)-aware status updates in vehicle-to-vehicle (V2V) communication, where each vehicle's status is represented by multiple interdependent packets. To enable fine-grained queue management at the packet level under resource constraints, we formulate a joint optimization problem that simultaneously learns active packet dropping and transmit power control strategies. A hybrid action space is designed to support both discrete dropping decisions and continuous power control. To exploit the graph-structured interference inherent in V2V topology, a graph neural network (GNN) is introduced to aggregate slowly varying large-scale fading, allowing agents to capture topological dependencies implicitly without frequent message exchange. The overall framework is built upon multi-agent proximal policy optimization (MAPPO), with centralized training and decentralized execution (CTDE). Simulations demonstrate that the proposed method significantly reduces average AoI across a wide range of network densities, channel conditions, and traffic loads, consistently outperforming several baselines.