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.

翻译：在无线多跳网络中最小化传输延迟是一项基础且具有挑战性的任务，这源于干扰、队列动态和分布式控制之间的复杂耦合。传统的调度算法，如最大权重或基于队列长度的策略，主要旨在优化吞吐量，但往往存在高延迟问题，特别是在异构或动态变化的拓扑中。近年来基于学习的方法，尤其是采用图神经网络（GNNs）的方法，在捕捉空间干扰结构方面显示出潜力。然而，传统的图卷积网络（GCNs）仍受限于其局部聚合机制以及无法建模冲突图中的长程依赖关系。为应对这些挑战，本文提出了一种基于Transformer GNN的面向延迟的分布式调度框架。该模型采用基于注意力的图编码器生成自适应每链路效用分数，以反映队列积压和干扰强度。随后，局部贪婪求解器（LGS）利用这些效用构建一个可行的独立链路集进行传输，确保分布式且无冲突的调度。