论文标题
通过反事实优化图神经网络的无线电源控制
Wireless Power Control via Counterfactual Optimization of Graph Neural Networks
论文作者
论文摘要
我们考虑了无线网络中下行链路功率控制的问题，该问题由多个发射器收借托对组成，这些发射机对彼此在单个共享的无线介质中相互通信。为了减轻并发传输之间的干扰，我们利用网络拓扑来创建图形神经网络体系结构，然后我们使用一种无监督的原始双重反事实优化方法来学习最佳功率分配决策。我们展示了反事实优化技术如何使我们保证最低率约束，该限制适应网络大小，因此在整个网络配置范围内达到了平均平均值至$ 5^{th} $百分位数用户率。
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.
