Name
Title | ||
|---|---|---|
UNSUPERVISED LEARNING FOR ASYNCHRONOUS RESOURCE ALLOCATION IN AD-HOC WIRELESS NETWORKS |
Abstract | ||
|---|---|---|
We consider optimal resource allocation problems under asynchronous wireless network setting. Without explicit model knowledge, we design an unsupervised learning method based on Aggregation Graph Neural Networks (Agg-GNNs). Depending on the localized aggregated information structure on each network node, the method can be learned globally and asynchronously while implemented locally. We capture the asynchrony by modeling the activation pattern as a characteristic of each node and train a policy-based power allocation method. We also propose a permutation invariance property which indicates the transferability of the trained Agg-GNN. We finally verify our strategy by numerical simulations compared with baseline methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICASSP39728.2021.9414181 | 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) |
Keywords | DocType | Citations |
Resource allocation, asynchronous, decentralized, graph neural networks | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiyang Wang | 1 | 2 | 2.38 |
| Mark Eisen | 2 | 64 | 10.51 |
| Alejandro Ribeiro | 3 | 15 | 6.75 |