Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks
This addresses scalability issues in dense wireless multi-hop networks, offering a practical improvement for network efficiency and resource management.
The paper tackles the high overhead of distributed scheduling in dense wireless networks by proposing a distributed link sparsification scheme using graph convolutional networks (GCNs), which reduces scheduling overhead while retaining 70% of network capacity with only 0.4% of message complexity and 2.6% of interfering neighbors compared to a baseline.
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.