Sep 12, 2021 · We propose fast heuristics based on graph convolutional networks (GCNs) that can be implemented in centralized and distributed manners.
Jun 12, 2023 · Specifically: 1) We propose the first GCN-based distributed MWIS solver for link scheduling by combining the topology-awareness of GCNs and the ...
We propose fast heuristics based on graph convolutional networks (GCNs) that can be implemented in centralized and distributed manners.
Abstract—Efficient scheduling of transmissions is a key prob- lem in wireless networks. The main challenge stems from the fact that optimal link scheduling ...
Link Scheduling Using Graph Neural Networks - ACM Digital Library
dl.acm.org › doi › TWC.2022.3222781
The main challenge stems from the fact that optimal link scheduling involves solving a maximum weighted independent set (MWIS) problem, which is known to be NP- ...
Efficient scheduling of transmissions is a key problem in wireless networks. The main challenge stems from the fact that optimal link scheduling involves ...
Nov 18, 2020 · We propose a distributed MWIS solver based on graph convolutional networks (GCNs). In a nutshell, a trainable GCN module learns topology-aware node embeddings.
This work proposes a distributed MWIS solver based on graph convolutional networks (GCNs) that learns topology-aware node embeddings that are combined with ...
The main challenge stems from the fact that optimal link scheduling involves solving a maximum weighted independent set (MWIS) problem, which is NP-hard. For ...
We consider the problem of binary power control, or link scheduling, in wireless interference networks, where the power control policy is trained using graph ...