The potential advantages of intelligent wireless communications with millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) are all based on the availability of instantaneous channel state information (CSI) at the base station (BS). However, in frequency division duplex (FDD) systems, no existence of channel reciprocity leads to the difficult acquisition of accurate CSI at the BS. In recent years, many researchers explored effective architectures based on deep learning (DL) to solve this problem and proved the success of DL-based solutions. However, existing schemes focused on the acquisition of complete CSI while ignoring the beamforming and precoding operations. In this paper, we propose an intelligent channel feedback architecture designed for beamforming based on attention mechanism and eigen features. That is, we design an eigenmatrix and eigenvector feedback neural network, called EMEVNet. The key idea of EMEVNet is to extract and feedback effective information meeting the requirements of beamforming and precoding operations at the BS. With the help of the attention mechanism, the proposed EMEVNet can be considered as a dual channel auto-encoder, which is able to jointly encode the eigenmatrix and eigenvector into codewords. Hence, the EMEVNet consists of an encoder deployed at the user and the decoder at the BS. Each user first utilizes singular value decomposition (SVD) transformation to extract the eigen features from CSI, and then selects an appropriate encoder for a specific channel to generate feedback codewords.

翻译：智能无线通信与毫米波(mmWave)和大量多输入多输出功能(MIMO)的潜在优势都是基于基站(BS)的即时频道状态信息(CSI)的可用性。然而,在频率分割双曲(DFD)系统中,没有频道对等性导致难以在BS获得准确的 CSI。近年来,许多研究人员探索了基于深层次学习(DL)的有效结构,以解决该问题,并证明了DL解决方案的成功。然而,现有的计划侧重于获取完整的 CSI,而忽视Bamder和预编码操作。在本文件中,我们提议建立一个智能频道反馈结构,根据注意机制和eigendiplex(DFDDFD)系统(CFD)系统(CFDFD)系统(CFD)系统(CFD)系统(DFD)系统(CFD)系统(DFD)系统(CFD)系统(CS)系统(CEVDEDER)系统(EVD)运行双向B-EVD(C)系统(O(C)系统(OD)数据库(OINS(EVD)提供双解码(O)系统(O-ID)数据库(OD)的计算和ODR)数据库(S)的计算。