Name
Title | ||
|---|---|---|
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 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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-92511-6_9 | VALUETOOLS |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. Ramakrishnan | 1 | 3 | 2.44 |
| Jaswanthi Mandalapu | 2 | 0 | 0.34 |
| Subrahmanya Swamy Peruru | 3 | 0 | 0.34 |
| Bhavesh Jain | 4 | 0 | 0.34 |
| Eitan Altman | 5 | 25 | 4.10 |