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.

翻译：当基站具备下行信道状态信息（CSI）时，可充分发挥频分双工（FDD）模式下大规模多输入多输出（MIMO）系统的巨大潜力。本文提出一种基于深度学习的联合信道估计与反馈框架，以实现大规模MIMO系统中的信道估计与反馈。具体而言，我们采用传统信道设计方案而非端到端方法。该模型包含两个网络：第一个为信道估计网络，采用双损失设计，可在消除信道噪声的同时精确估计完整信道信息；第二个为压缩与反馈网络。受掩码令牌Transformer启发，我们提出一种可学习的掩码令牌方法，以获得优异的估计与压缩性能。大量仿真结果与消融研究表明，在分离任务与联合任务中，本方法均优于现有最先进的信道估计与反馈方法。