Multi-task Deep Neural Networks for Massive MIMO CSI Feedback

TL;DR

This paper introduces a multi-task deep learning approach for CSI feedback in massive MIMO systems, reducing training costs and storage needs while maintaining high performance.

Contribution

It proposes a multi-task learning framework with an encoder-shared architecture to improve CSI feedback efficiency across multiple scenarios.

Findings

Achieves comprehensive feedback performance

Reduces training costs significantly

Lowers storage requirements for the model

Abstract

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.