用图神经网络稀疏化链路，降低无线网络调度开销。
Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks (Journal Version)
- 用GNN动态调整链路竞争阈值，减少无效调度
- 在500链路网络中降低拥塞与无线电覆盖范围
- 适合延迟容忍型流量的高效分布式调度
在密集连接的无线网络中，分布式链路调度算法产生的显著信令开销会加剧拥塞、能耗和无线电覆盖范围扩大问题。为此，我们提出一种基于图神经网络（GNN）的分布式链路稀疏化方案，在保持网络容量的前提下，降低延迟容忍型流量的调度开销。GNN模块根据流量统计与网络拓扑，动态调整各链路的争用阈值，使低成功概率链路主动退出调度竞争。该方法依托一种新型离线约束无监督学习算法，有效平衡调度开销最小化与总效用达标两个目标。在最多含500个链路的模拟多跳无线网络中，该链路稀疏化技术对四种不同分布式链路调度协议均有效缓解了网络拥塞，并减少了无线电覆盖范围。
原文摘要 · Abstract (English)
In wireless networks characterized by dense connectivity, the significant signaling overhead generated by distributed link scheduling algorithms can exacerbate issues like congestion, energy consumption, and radio footprint expansion. To mitigate these challenges, we propose a distributed link sparsification scheme employing graph neural networks (GNNs) to reduce scheduling overhead for delay-tolerant traffic while maintaining network capacity. A GNN module is trained to adjust contention thresholds for individual links based on traffic statistics and network topology, enabling links to withdraw from scheduling contention when they are unlikely to succeed. Our approach is facilitated by a novel offline constrained {unsupervised} learning algorithm capable of balancing two competing objectives: minimizing scheduling overhead while ensuring that total utility meets the required level. In simulated wireless multi-hop networks with up to 500 links, our link sparsification technique effectively alleviates network congestion and reduces radio footprints across four distinct distributed link scheduling protocols.
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