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.

翻译：在频度司(FDD)大规模多投入多产出产出系统中,对频道国家信息的反馈广泛应用了深层次学习,对于典型的反馈模式监督培训,很难满足大量任务专用标签数据的要求,在多种情况下该模式的巨大培训成本和储存使用妨碍了模式的应用。在本信内,提议采用多任务学习方法,以提高反馈网络的可行性。还提议采用一个编码共享反馈架构和相应的培训计划,以促进多任务学习方法的实施。实验结果表明,拟议的多任务学习方法可以实现全面的反馈业绩,大大减少培训成本和反馈模式的储存使用。