论文标题
无线电源控制的对比度自我监督学习
Contrastive Self-Supervised Learning for Wireless Power Control
论文作者
论文摘要
我们提出了一种使用自我监督的学习在无线网络中进行电力控制的新方法。 We partition a multi-layer perceptron that takes as input the channel matrix and outputs the power control decisions into a backbone and a head, and we show how we can use contrastive learning to pre-train the backbone so that it produces similar embeddings at its output for similar channel matrices and vice versa, where similarity is defined in an information-theoretic sense by identifying the interference links that can be optimally treated as noise.然后使用有限数量的标记样品对主链和头部进行微调。仿真结果表明了所提出的方法的有效性，证明了在求和效率和样本效率中的纯监督学习方法上的显着增长。
We propose a new approach for power control in wireless networks using self-supervised learning. We partition a multi-layer perceptron that takes as input the channel matrix and outputs the power control decisions into a backbone and a head, and we show how we can use contrastive learning to pre-train the backbone so that it produces similar embeddings at its output for similar channel matrices and vice versa, where similarity is defined in an information-theoretic sense by identifying the interference links that can be optimally treated as noise. The backbone and the head are then fine-tuned using a limited number of labeled samples. Simulation results show the effectiveness of the proposed approach, demonstrating significant gains over pure supervised learning methods in both sum-throughput and sample efficiency.
