Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks

• URL: http://arxiv.org/abs/2203.14339v1
• Date: Sun, 27 Mar 2022 16:02:12 GMT
• Title: Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks
• Authors: Zhongyuan Zhao, Ananthram Swami, Santiago Segarra
• Abstract summary: We propose a distributed scheme for link sparsification with graph convolutional networks (GCNs) In medium-sized wireless networks, our proposed sparse scheduler beats threshold-based sparsification policies by retaining almost $70%$ of the total capacity achieved by a greedy scheduler.
• Score: 37.84368235950714
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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