Wireless Power Control via Counterfactual Optimization of Graph Neural Networks

• URL: http://arxiv.org/abs/2002.07631v1
• Date: Mon, 17 Feb 2020 07:54:39 GMT
• Title: Wireless Power Control via Counterfactual Optimization of Graph Neural Networks
• Authors: Navid Naderializadeh, Mark Eisen, Alejandro Ribeiro
• Abstract summary: We consider the problem of downlink power control in wireless networks, consisting of multiple transmitter-receiver pairs communicating 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. We then use an unsupervised primal-dual counterfactual optimization approach to learn optimal power allocation decisions.
• Score: 124.89036526192268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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