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arXiv · 2111.00459

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

S. Ramakrishnan·Jaswanthi Mandalapu·Subrahmanya Swamy Peruru·Bhavesh Jain·Eitan Altman
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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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BibTeXRIS

S. Ramakrishnan, Jaswanthi Mandalapu, Subrahmanya Swamy Peruru, Bhavesh Jain, Eitan Altman. 2021-10-31. Graph Neural Network based scheduling : Improved throughput under a generalized interference model. https://arxiv.org/abs/2111.00459

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