论文标题
分布式链接稀疏用于使用图形神经网络可扩展调度
Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks
论文作者
论文摘要
密集的无线多跳网网络中用于吞吐量或实用性最大化的分布式调度算法可能具有压倒性的高架开销，从而导致交通拥堵，能源消耗，无线电足迹和安全性脆弱性增加。对于具有密度连接性的无线网络，我们提出了一个分布式方案，用于与图形卷积网络（GCN）链接稀疏性，该方案可以减少大部分网络容量，同时减少调度上的开销。简而言之，可训练的GCN模块会生成节点嵌入，作为拓扑感知和可重复使用的参数，用于本地决策机制，如果链接不太可能获胜，则可以从计划中撤回该链接。在中型无线网络中，我们提议的稀疏调度程序通过保留了分布式贪婪的最大关键调整器所实现的总计$ 70 \％$ $ 0.4 \％\％的点对点消息复杂性和2.6 \ $ 2.6 \％的平均邻居数量的$ 2.6 \％。
Distributed scheduling algorithms for throughput or utility maximization in dense wireless multi-hop networks can have overwhelmingly high overhead, causing increased congestion, energy consumption, radio footprint, and security vulnerability. For wireless networks with dense connectivity, we propose a distributed scheme for link sparsification with graph convolutional networks (GCNs), which can reduce the scheduling overhead while keeping most of the network capacity. In a nutshell, a trainable GCN module generates node embeddings as topology-aware and reusable parameters for a local decision mechanism, based on which a link can withdraw itself from the scheduling contention if it is not likely to win. In medium-sized wireless networks, our proposed sparse scheduler beats classical threshold-based sparsification policies by retaining almost $70\%$ of the total capacity achieved by a distributed greedy max-weight scheduler with $0.4\%$ of the point-to-point message complexity and $2.6\%$ of the average number of interfering neighbors per link.
