A Blind CSI Prediction Method Based on Deep Learning for V2I Millimeter-Wave Channel

With the development of the Internet of vehicles and 5G, there emerge more and more challenging application scenarios with fast time-varying channels and high mobility nodes, such as high speed trains environment and vehicle-to-infrastructure (V2I) communication in highway. To support the reliable vehicular communication and mobile edge computing (MEC), it is important to obtain the future channel state information (CSI), which can help optimize system transmission scheme. In this paper, we propose an efficient blind CSI prediction model, called BCPMN. We first reshape the sampled signal into a specific 2-dimensional matrix. Then we propose a learning framework contains of convolutional neural network (CNN), long short-term memory (LSTM) network and fully connected layers. To validate the proposed model, we conduct extensive experiment in three modulation modes. The results show that the BCPMN achieves highly accurate signal-to-noise ratio (SNR) prediction in the fast changing channel model with different modulation modes. In particular, the proposed model can obtain better performance than other methods, and can achieve better performance than other methods without the payload cost of pilot.