CSI Intelligent Feedback for Massive MIMO Systems in V2I Scenarios

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 multiinput 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).