Multi-task Deep Neural Networks for Massive MIMO CSI Feedback
This work addresses efficiency issues for wireless communication systems, but it is incremental as it builds on existing deep learning methods for CSI feedback.
The paper tackles the problem of high training costs and storage requirements for deep learning-based channel state information feedback in massive MIMO systems by proposing a multi-task learning approach, which achieves comprehensive feedback performance while significantly reducing these costs.
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.