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Performance Evaluation Methodologies and Tools. 14th EAI International Conference, VALUETOOLS 2021, Virtual Event, October 30–31, 2021, Proceedings

Research Article

Graph Neural Network Based Scheduling: Improved Throughput Under a Generalized Interference Model

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  • @INPROCEEDINGS{10.1007/978-3-030-92511-6_9,
        author={Ramakrishnan Sambamoorthy and Jaswanthi Mandalapu and Subrahmanya Swamy Peruru and Bhavesh Jain and Eitan Altman},
        title={Graph Neural Network Based Scheduling: Improved Throughput Under a Generalized Interference Model},
        proceedings={Performance Evaluation Methodologies and Tools. 14th EAI International Conference, VALUETOOLS 2021, Virtual Event, October 30--31, 2021, Proceedings},
        proceedings_a={VALUETOOLS},
        year={2021},
        month={12},
        keywords={Resource allocation Graph Convolutional Neural Networks Adhoc networks},
        doi={10.1007/978-3-030-92511-6_9}
    }
    
  • Ramakrishnan Sambamoorthy
    Jaswanthi Mandalapu
    Subrahmanya Swamy Peruru
    Bhavesh Jain
    Eitan Altman
    Year: 2021
    Graph Neural Network Based Scheduling: Improved Throughput Under a Generalized Interference Model
    VALUETOOLS
    Springer
    DOI: 10.1007/978-3-030-92511-6_9
Ramakrishnan Sambamoorthy,*, Jaswanthi Mandalapu, Subrahmanya Swamy Peruru, Bhavesh Jain, Eitan Altman
    *Contact email: ramakrishnan.sambamoorthy@inria.fr

    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 thek-tolerant conflict graph model and design an efficient approximation for the well-known 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%) improve the performance of the conventional greedy approach.

    Keywords
    Resource allocation Graph Convolutional Neural Networks Adhoc networks
    Published
    2021-12-08
    Appears in
    SpringerLink
    http://dx.doi.org/10.1007/978-3-030-92511-6_9
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