Wireless Power Control via Counterfactual Optimization of Graph Neural Networks
This addresses interference management in wireless networks for improved user rates, but it is incremental as it builds on existing graph neural network and optimization methods.
The paper tackles the problem of downlink power control in wireless networks to mitigate interference among concurrent transmissions, achieving a balance between average and 5th percentile user rates across various network configurations.
We consider the problem of downlink power control in wireless networks, consisting of multiple transmitter-receiver pairs communicating with each other over a single shared wireless medium. To mitigate the interference among concurrent transmissions, we leverage the network topology to create a graph neural network architecture, and we then use an unsupervised primal-dual counterfactual optimization approach to learn optimal power allocation decisions. We show how the counterfactual optimization technique allows us to guarantee a minimum rate constraint, which adapts to the network size, hence achieving the right balance between average and $5^{th}$ percentile user rates throughout a range of network configurations.