Wireless network energy saving with graph neural networks

Abstract

The present disclosure discusses network energy savings (NES) machine learning (ML) models that predict NES parameters used to adjust control parameters of respective network nodes in a wireless network, wherein the NES parameters can be used by the respective network nodes to adjust their control parameters, such that the wireless network realizes or achieves NES as a whole. The wireless network is represented as a graph with heterogeneous vertices that represent corresponding network nodes and edges that represent connections between the network nodes. The NES ML model comprises a graph neural network (GNN) and a fully connected neural network (FCNN). The GNN may be a graph convolutional neural network or a graph attention network. The FCNN may be a multi-layer perceptron, a deep neural network, and/or some other type of neural network. Other embodiments may be described and/or claimed.

Dependencies are inferred from the claim text.