2022/08/13 by Yibin Zhang, Jinlong Sun, Zhang, Yibin +9
Engineering · #Antenna Design and Analysis #Base station #Beamforming #Channel state information #Computer network #Computer science #Electronic engineering #Encoder #Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #MIMO #Microwave Engineering and Waveguides #Millimeter-Wave Propagation and Modeling #Precoding #Robustness (evolution) #Signal Processing (eess.SP) #Telecommunications #Wireless #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2208.06570
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2022/08/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The potential advantages of intelligent wireless communications with millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) are based on the availability of instantaneous channel state information (CSI) at the base station (BS). However, no existence of channel reciprocity leads to the difficult acquisition of accurate CSI at the BS in frequency division duplex (FDD) systems. 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 using eigenmatrix and eigenvector feedback neural network (EMEVNet). 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. Simulation results show great performance improvement and robustness with extremely low overhead of the proposed EMEVNet method compared with the traditional DL-based CSI feedback methods.