Prediction of V2V channel quality under double-Rayleigh fading channels

The V2V (Vehicle to Vehicle) communication system in the Internet of vehicular networking is an important part of the future intelligent vehicle network, and it is extremely important to study the quality of the V2V communication channel. The double-Rayleigh fading model can better reflect the small-scale fading characteristics of the V2V channel. Therefore, this paper conducts experimental verification in this channel environment. First, using the gain matrix constructed by CSI, the images of time, frequency and related domain are obtained through the transformation of contour line, waterfall map and related diagram. Then, The image features transformed by the channel state information are extracted based on the improved multi-texton histogram. Finally, the V2V channel quality under slow fading conditions is discriminated under the SVM. The results show that the method not only simplifies the difficulty of channel state feature extraction, but also can effectively and reliably predict the instantaneous channel state.