We propose a method for channel training and precoding in FDD massive MIMO based on deep neural networks (DNNs), exploiting Downlink (DL) channel covariance knowledge. The DNN is optimized to maximize the DL multi-user sum-rate, by producing a pre-beamforming matrix based on user channel covariances that maps the original channel vectors to effective channels. Measurements of these effective channels are received at the users via common pilot transmission and sent back to the base station (BS) through analog feedback without further processing. The BS estimates the effective channels from received feedback and constructs a linear precoder by concatenating the optimized pre-beamforming matrix with a zero-forcing precoder over the effective channels. We show that the proposed method yields significantly higher sum-rates than the state-of-the-art DNN-based channel training and precoding scheme, especially in scenarios with small pilot and feedback size relative to the channel coherence block length. Unlike many works in the literature, our proposition does not involve deployment of a DNN at the user side, which typically comes at a high computational cost and parameter-transmission overhead on the system, and is therefore considerably more practical.

翻译：本文提出一种基于深度神经网络（DNN）的FDD大规模MIMO信道训练与预编码方法，该方法利用下行链路（DL）信道协方差知识。通过根据用户信道协方差生成预波束赋形矩阵（将原始信道向量映射至有效信道），该DNN以最大化下行多用户和速率为目标进行优化。用户通过公共导频传输接收这些有效信道的测量值，并通过模拟反馈直接发送回基站（BS），无需额外处理。BS根据接收到的反馈估计有效信道，并将优化后的预波束赋形矩阵与基于有效信道的迫零预编码器级联，从而构建线性预编码器。研究表明，所提方法相比现有最优的基于DNN的信道训练与预编码方案显著提升和速率，尤其在导频与反馈开销相对于信道相干块长度较小的场景中表现突出。与文献中诸多方法不同，本方案无需在用户端部署DNN（该部署通常伴随高计算成本与系统参数传输开销），因此具有显著更高的实用性。