CSI intelligent feedback for massive MIMO systems in V2I scenarios

Abstract

With the rapid development of the Internet of vehicles (IoV), vehicle to everything (V2X) has strict requirements for ultra-reliable and low latency communications (URLLC), and massive multi-input multi-output (MIMO) channel state information (CSI) feedback can effectively support URLLC communication in 5G vehicle to infrastructure (V2I) scenarios. Existing research applies deep learning (DL) to CSI feedback, but most of its algorithms are based on low-speed outdoor or indoor environments and assume that the feedback link is perfect. However, the actual channel still has the influence of additive noise and nonlinear effects, especially in the high-speed V2I scene, the channel characteristics are more complex and time-varying. In response to the above problems, this paper proposes a CSI intelligent feedback network model for V2I scenarios, named residual mixnet (RM-Net). The network learns the channel characteristics in the V2I scenario at the vehicle user (User Equipment, UE), compresses the CSI and sends it to the channel; the roadside base station (Base Station, BS) receives the data and learns the compressed data characteristics, and then restore the original CSI. The system simulation results show that the RM-Net training speed is fast, requires fewer training samples, and its performance is significantly better than the existing DL-based CSI feedback algorithm. It can learn channel characteristics in high-speed mobile V2I scenarios and overcome the influence of additive noise. At the same time, the network still has good performance under high compression ratio and low signal-to-noise ratio (SNR).