Multi-task Deep Neural Networks for Massive MIMO CSI Feedback
- URL: http://arxiv.org/abs/2204.12442v1
- Date: Mon, 18 Apr 2022 12:43:05 GMT
- Title: Multi-task Deep Neural Networks for Massive MIMO CSI Feedback
- Authors: Boyuan Zhang, Haozhen Li, Xin Liang, Xinyu Gu, Lin Zhang
- Abstract summary: A multi-task learning-based approach is proposed to improve the feasibility of the feedback network.
The experimental results indicate that the proposed multi-task learning approach can achieve comprehensive feedback performance.
- Score: 4.985679007615566
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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