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.

翻译：本文首次将自监督学习应用于数字天线阵列数据。编码器-解码器网络在数字阵列数据上进行预训练，以执行一种称为通道修复的自监督噪声重建任务，即网络推断被零掩码的阵列数据内容。该自监督步骤无需人工标注数据。随后，将预训练得到的编码器架构和权重迁移至具有任务特定解码器的新网络，并在少量标注数据上训练该新网络。研究表明，与从随机初始化开始、在相同标注数据上训练的等效网络相比，基于无标注数据预训练的编码器-解码器网络能够更有效地完成数字阵列数据的带宽回归任务。