The great potentials of massive Multiple-Input Multiple-Output (MIMO) in Frequency Division Duplex (FDD) mode can be fully exploited when the downlink Channel State Information (CSI) is available at base stations. However, the accurate CSI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas. In this paper, we propose a deep learning based joint channel estimation and feedback framework, which comprehensively realizes the estimation, compression, and reconstruction of downlink channels in FDD massive MIMO systems. Two networks are constructed to perform estimation and feedback explicitly and implicitly. The explicit network adopts a multi-Signal-to-Noise-Ratios (SNRs) technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels, while the implicit network directly compresses pilots and sends them back to reduce network parameters. Quantization module is also designed to generate data-bearing bitstreams. Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.

翻译：大规模多输入多输出(MIMO)在频分双工(FDD)模式下的巨大潜力，可在基站获取下行信道状态信息(CSI)时得到充分发挥。然而，由于海量天线带来的巨大反馈开销，准确获取CSI十分困难。本文提出一种基于深度学习的联合信道估计与反馈框架，全面实现了FDD大规模MIMO系统中下行信道的估计、压缩与重构。我们构建了两个网络分别进行显式与隐式的估计和反馈。显式网络采用多信噪比(SNRs)技术获得一个可适应不同SNR的单一训练信道估计子网，并利用深度残差网络重构信道；而隐式网络直接压缩导频并反馈，以减少网络参数。我们还设计了量化模块以生成承载数据的比特流。仿真结果表明，所提出的两个网络均展现出优异的信道重构性能，并对不同环境与量化误差具有鲁棒性。