  Scalability is a major concern in implementing deep learning (DL) based
methods in wireless communication systems. Given various channel reconstruction
tasks, applying one DL model for one specific task is costly in both model
training and model storage. In this paper, we propose a novel unsupervised deep
plug-and-play prior method for three channel reconstruction tasks in the
downlink of massive multiple-input multiple-output (MIMO) systems, including
channel estimation, antenna extrapolation and channel state information (CSI)
feedback. The proposed method corresponding to these three channel
reconstruction tasks employs a common DL model, which greatly reduces the
overhead of model training and storage. Unlike general multi-task learning, the
DL model of the proposed method does not require further fine-tuning for
specific channel reconstruction tasks. Extensive experiments are conducted on
the DeepMIMO dataset to demonstrate the convergence, performance, and storage
overhead of the proposed method for the three channel reconstruction tasks.
