| Paper ID | SS-3.6 |
| Paper Title |
UNSUPERVISED LEARNING FOR ASYNCHRONOUS RESOURCE ALLOCATION IN AD-HOC WIRELESS NETWORKS |
| Authors |
Zhiyang Wang, University of Pennsylvania, United States; Mark Eisen, Intel Labs, United States; Alejandro Ribeiro, University of Pennsylvania, United States |
| Session | SS-3: Machine Learning in Wireless Networks |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Special Sessions: Machine Learning in Wireless Networks |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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 resource 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. |
