When the base station has downlink channel status information (CSI), the huge potential of large-scale multiple input multiple output (MIMO) in frequency division duplex (FDD) mode can be fully exploited. In this paper, we propose a deep-learning-based joint channel estimation and feedback framework to realize channel estimation and feedback in massive MIMO systems. Specifically, we use traditional channel design rather than end-to-end methods. Our model contains two networks. The first network is a channel estimation network, which adopts a double loss design, and can accurately estimate the full channel information while removing channel noises. The second network is a compression and feedback network. Inspired by the masked token transformer, we propose a learnable mask token method to obtain excellent estimation and compression performance. The extensive simulation results and ablation studies show that our method outperforms state-of-the-art channel estimation and feedback methods in both separate and joint tasks.

本文提出了一种基于深度学习的联合信道估计和反馈框架，用传统信道设计代替端到端方法实现超大规模MIMO系统的信道估计和反馈，其中包含两个网络：一个信道估计网络和一个压缩反馈网络，大量模拟结果和消融研究表明我们的方法优于目前的信道估计和反馈方法。

基于蒙版的 Token Transformer 在大规模 MIMO 系统中的联合信道估计与反馈