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Conference papers

Graph Neural Network based scheduling : Improved throughput under a generalized interference model

Abstract : In this work, we propose a Graph Convolutional Neural Networks (GCN) based scheduling algorithm for adhoc networks. In particular, we consider a generalized interference model called the k-tolerant conflict graph model and design an efficient approximation for the wellknown Max-Weight scheduling algorithm. A notable feature of this work is that the proposed method do not require labelled data set (NP-hard to compute) for training the neural network. Instead, we design a loss function that utilises the existing greedy approaches and trains a GCN that improves the performance of greedy approaches. Our extensive numerical experiments illustrate that using our GCN approach, we can significantly (4-20 percent) improve the performance of the conventional greedy approach.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-03410462
Contributor : Eitan Altman Connect in order to contact the contributor
Submitted on : Sunday, October 31, 2021 - 8:59:13 PM
Last modification on : Friday, May 6, 2022 - 3:46:50 AM
Long-term archiving on: : Tuesday, February 1, 2022 - 6:29:33 PM

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  • HAL Id : hal-03410462, version 1

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Ramakrishnan Sambamoorthy, Jaswanthi Mandalapu, Subrahmanya Peruru, Bhavesh Jain, Eitan Altman. Graph Neural Network based scheduling : Improved throughput under a generalized interference model. EAI - Valuetools, Oct 2021, Guangzhou, China. ⟨hal-03410462⟩

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