Delay-Oriented Distributed Scheduling with TransGNN

TL;DR

This paper introduces a delay-oriented distributed scheduling method using Transformer GNNs, which effectively captures long-range dependencies in wireless networks to reduce transmission delay.

Contribution

It proposes a novel Transformer GNN-based framework with an attention mechanism for adaptive utility scoring, improving delay performance in distributed wireless scheduling.

Findings

Enhanced scheduling delay performance demonstrated in simulations.

Effective modeling of long-range dependencies in interference graphs.

Distributed implementation with conflict-free link selection.

Abstract

Minimizing transmission delay in wireless multi-hop networks is a fundamental yet challenging task due to the complex coupling among interference, queue dynamics, and distributed control. Traditional scheduling algorithms, such as max-weight or queue-length-based policies, primarily aim to optimize throughput but often suffer from high latency, especially in heterogeneous or dynamically changing topologies. Recent learning-based approaches, particularly those employing Graph Neural Networks (GNNs), have shown promise in capturing spatial interference structures. However, conventional Graph Convolutional Networks (GCNs) remain limited by their local aggregation mechanism and their inability to model long-range dependencies within the conflict graph. To address these challenges, this paper proposes a delay-oriented distributed scheduling framework based on Transformer GNN. The proposed model employs an attention-based graph encoder to generate adaptive per-link utility scores that reflect both queue backlog and interference intensity. A Local Greedy Solver (LGS) then utilizes these utilities to construct a feasible independent set of links for transmission, ensuring distributed and conflict-free scheduling.