This work presents the first applications of self-supervised learning applied to data from digital antenna arrays. Encoder-decoder networks are pretrained on digital array data to perform a self-supervised noisy-reconstruction task called channel in-painting, in which the network infers the contents of array data that has been masked with zeros. The self-supervised step requires no human-labeled data. The encoder architecture and weights from pretraining are then transferred to a new network with a task-specific decoder, and the new network is trained on a small volume of labeled data. We show that pretraining on the unlabeled data allows the new network to perform the task of bandwidth regression on the digital array data better than an equivalent network that is trained on the same labeled data from random initialization.

本研究介绍了自监督学习在数字天线阵列数据上的首次应用。通过对数字阵列数据进行预训练的编码器-解码器网络执行了一种自监督的噪声恢复任务，称为信道修复，通过推断被零掩盖的阵列数据的内容。然后，将编码器的架构和权重转移到一个具有特定任务解码器的新网络上，并使用少量标记数据对新网络进行训练。研究结果表明，在未标记数据上进行预训练可以使新网络在数字阵列数据上执行带宽回归任务，比从随机初始化进行训练的等效网络效果更好。

用于自监督预训练和数字天线阵列下行信号带宽回归的编码器-解码器网络