Information!

Updated at best once per year, likely missing cool new stuff !

Dynamic Radio Ressource Management

In this project we are developing methodologies and techniques for zero-touch autonomous network configuration. We design novel algorithms having practical relevance, that we deploy on real network.

At a glance

Channel allocation

Leveraging AC data, we optimize AP channel and bondings settings, and contrast it to state of the art Cisco TurboCA in simulation [NETWORKING-21] and real deployment [COMMAG-22].

Size of AppClassNet compared to other public datasets

Power management

Leveraging IEEE 802.11k data, we optimize AP power settings and contrast it to state of the art Cisco TPCv1 [TECHREP-22-WLANpowerControl] in real deployment.

Transformer-based RRM

Leveraging progress in neural architectures, we explore transformer based architectures with self-attention mechanism [AAAI-22] for the purpose of (power and) channel allocation.

GNN-based inference

We contrast Graph Neural Networks with classical recurrent neural architecture for the purpose of estimating WLAN interference [CoNEXT-GNN-22] .

References