  Deep learning has been widely applied for the channel state information (CSI)
feedback in frequency division duplexing (FDD) massive multiple-input
multiple-output (MIMO) system. For the typical supervised training of the
feedback model, the requirements of large amounts of task-specific labeled data
can hardly be satisfied, and the huge training costs and storage usage of the
model in multiple scenarios are hindrance for model application. In this
letter, a multi-task learning-based approach is proposed to improve the
feasibility of the feedback network. An encoder-shared feedback architecture
and the corresponding training scheme are further proposed to facilitate the
implementation of the multi-task learning approach. The experimental results
indicate that the proposed multi-task learning approach can achieve
comprehensive feedback performance with considerable reduction of training cost
and storage usage of the feedback model.
