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

• URL: http://arxiv.org/abs/2111.00459v1
• Date: Sun, 31 Oct 2021 10:36:11 GMT
• Title: Graph Neural Network based scheduling : Improved throughput under a generalized interference model
• Authors: S. Ramakrishnan, Jaswanthi Mandalapu, Subrahmanya Swamy Peruru, Bhavesh Jain, Eitan Altman
• Abstract summary: We propose a Graph Convolutional Neural Networks (GCN) based scheduling algorithm for adhoc networks. 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.
• Score: 3.911413922612859
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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 ($4$-$20$ percent) improve the performance of the conventional greedy approach.

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