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Conference Papers Year : 2021

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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Dates and versions

hal-03410462 , version 1 (31-10-2021)

Identifiers

  • HAL Id : hal-03410462 , version 1

Cite

Ramakrishnan Sambamoorthy, Jaswanthi Mandalapu, Subrahmanya Swamy 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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