Graph Neural Network based scheduling : Improved throughput under a generalized interference model
This work addresses scheduling efficiency in adhoc networks, offering a practical improvement over existing greedy approaches, though it is incremental as it builds on known methods.
The authors tackled the problem of scheduling in adhoc networks under a generalized interference model by proposing a Graph Convolutional Neural Network (GCN)-based algorithm that improves throughput without requiring labeled data, achieving a 4-20% performance gain over conventional greedy methods.
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.